3gpp Pathloss
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Technical Report
3rd Generation Partnership Project;
Technical Specification Group Radio Access Network;
Study on channel model for frequencies from 0.5 to 100 GHz
(Release 17)
The present document has been developed within the 3rd Generation Partnership Project (3GPP TM) and may be further elaborated for the purposes of 3GPP.
The present document has not been subject to any approval process by the 3GPP Organizational Partners and shall not be implemented.
This Specification is provided for future development work within 3GPP only. The Organizational Partners accept no liability for any use of this Specification.
Specifications and Reports for implementation of the 3GPP TM system should be obtained via the 3GPP Organizational Partners' Publications Offices.
Release 17 2 3GPP TR 38.901 V17.0.0 (2022-03)
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3GPP
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Contents
1 Scope ........................................................................................................................................................ 7
2 References ................................................................................................................................................ 7
3 Definitions, symbols and abbreviations ................................................................................................... 8
3.1 Definitions ......................................................................................................................................................... 8
3.2 Symbols ............................................................................................................................................................. 8
3.3 Abbreviations ..................................................................................................................................................... 9
4 Introduction ............................................................................................................................................ 10
5 Void ........................................................................................................................................................ 11
6 Status/expectation of existing information on high frequencies ............................................................ 11
6.1 Channel modelling works outside of 3GPP ..................................................................................................... 11
6.2 Scenarios of interest ......................................................................................................................................... 13
6.3 Channel measurement capabilities ................................................................................................................... 14
6.4 Modelling objectives........................................................................................................................................ 15
7 Channel model(s) for 0.5-100 GHz ........................................................................................................ 15
7.1 Coordinate system ........................................................................................................................................... 15
7.1.1 Definition ................................................................................................................................................... 15
7.1.2 Local and global coordinate systems .......................................................................................................... 16
7.1.3 Transformation from a LCS to a GCS ........................................................................................................ 16
7.1.4 Transformation from an LCS to a GCS for downtilt angle only ................................................................ 20
7.2 Scenarios .......................................................................................................................................................... 21
7.3 Antenna modelling ........................................................................................................................................... 24
7.3.1 Antenna port mapping ................................................................................................................................ 25
7.3.2 Polarized antenna modelling ...................................................................................................................... 25
7.4 Pathloss, LOS probability and penetration modelling ..................................................................................... 27
7.4.1 Pathloss ...................................................................................................................................................... 27
7.4.2 LOS probability .......................................................................................................................................... 30
7.4.3 O2I penetration loss ................................................................................................................................... 31
7.4.3.1 O2I building penetration loss................................................................................................................ 31
7.4.3.2 O2I car penetration loss ........................................................................................................................ 32
7.4.4 Autocorrelation of shadow fading .............................................................................................................. 32
7.5 Fast fading model ............................................................................................................................................ 33
7.6 Additional modelling components ................................................................................................................... 48
7.6.1 Oxygen absorption ..................................................................................................................................... 48
7.6.2 Large bandwidth and large antenna array .................................................................................................. 49
7.6.2.1 Modelling of the propagation delay ...................................................................................................... 49
7.6.2.2 Modelling of intra-cluster angular and delay spreads ........................................................................... 49
7.6.3 Spatial consistency ..................................................................................................................................... 50
7.6.3.1 Spatial consistency procedure............................................................................................................... 51
7.6.3.2 Spatially-consistent UT/BS mobility modelling ................................................................................... 51
7.6.3.3 LOS/NLOS, indoor states and O2I parameters .................................................................................... 55
7.6.3.4 Applicability of spatial consistency ...................................................................................................... 56
7.6.4 Blockage..................................................................................................................................................... 57
7.6.4.1 Blockage model A ................................................................................................................................ 58
7.6.4.2 Blockage model B ................................................................................................................................ 60
7.6.5 Correlation modelling for multi-frequency simulations ............................................................................. 62
7.6.5.1 Alternative channel generation method ................................................................................................................ 63
7.6.6 Time-varying Doppler shift ........................................................................................................................ 65
7.6.7 UT rotation ................................................................................................................................................. 65
7.6.8 Explicit ground reflection model ................................................................................................................ 65
7.6.9 Absolute time of arrival.............................................................................................................................. 68
7.6.10 Dual mobility ............................................................................................................................................. 69
7.6.11 Sources of EM interference ........................................................................................................................ 69
7.6.12 Embedded devices ...................................................................................................................................... 69
7.7 Channel models for link-level evaluations....................................................................................................... 70
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7.7.1 Clustered Delay Line (CDL) models .......................................................................................................... 70
7.7.2 Tapped Delay Line (TDL) models ............................................................................................................. 73
7.7.3 Scaling of delays ........................................................................................................................................ 76
7.7.4 Spatial filter for generating TDL channel model........................................................................................ 77
7.7.4.1 Exemplary filters/antenna patterns ....................................................................................................... 77
7.7.4.2 Generation procedure ........................................................................................................................... 78
7.7.5 Extension for MIMO simulations............................................................................................................... 78
7.7.5.1 CDL extension: Scaling of angles ........................................................................................................ 78
7.7.5.2 TDL extension: Applying a correlation matrix ..................................................................................... 79
7.7.6 K-factor for LOS channel models .............................................................................................................. 80
7.8 Channel model calibration ............................................................................................................................... 80
7.8.1 Large scale calibration ............................................................................................................................... 80
7.8.2 Full calibration ........................................................................................................................................... 81
7.8.3 Calibration of additional features ............................................................................................................... 82
7.8.4 Calibration of the indoor factory scenario .................................................................................................. 84
8 Map-based hybrid channel model (Alternative channel model methodology) ...................................... 85
8.1 Coordinate system ........................................................................................................................................... 86
8.2 Scenarios .......................................................................................................................................................... 86
8.3 Antenna modelling ........................................................................................................................................... 86
8.4 Channel generation .......................................................................................................................................... 86
Annex A: Further parameter definitions ............................................................................................. 97
A.1 Calculation of angular spread .......................................................................................................................... 97
A.2 Calculation of mean angle ............................................................................................................................... 97
Annex B: Change history ...................................................................................................................... 98
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Foreword
This Technical Report has been produced by the 3rd Generation Partnership Project (3GPP).
The contents of the present document are subject to continuing work within the TSG and may change following formal
TSG approval. Should the TSG modify the contents of the present document, it will be re-released by the TSG with an
identifying change of release date and an increase in version number as follows:
Version x.y.z
where:
x the first digit:
1 presented to TSG for information;
2 presented to TSG for approval;
3 or greater indicates TSG approved document under change control.
y the second digit is incremented for all changes of substance, i.e. technical enhancements, corrections,
updates, etc.
z the third digit is incremented when editorial only changes have been incorporated in the document.
In the present document, modal verbs have the following meanings:
shall indicates a mandatory requirement to do something
shall not indicates an interdiction (prohibition) to do something
The constructions "shall" and "shall not" are confined to the context of normative provisions, and do not appear in
Technical Reports.
The constructions "must" and "must not" are not used as substitutes for "shall" and "shall not". Their use is avoided
insofar as possible, and they are not used in a normative context except in a direct citation from an external, referenced,
non-3GPP document, or so as to maintain continuity of style when extending or modifying the provisions of such a
referenced document.
should indicates a recommendation to do something
should not indicates a recommendation not to do something
may indicates permission to do something
need not indicates permission not to do something
The construction "may not" is ambiguous and is not used in normative elements. The unambiguous constructions
"might not" or "shall not" are used instead, depending upon the meaning intended.
can indicates that something is possible
cannot indicates that something is impossible
The constructions "can" and "cannot" are not substitutes for "may" and "need not".
will indicates that something is certain or expected to happen as a result of action taken by an agency
the behaviour of which is outside the scope of the present document
will not indicates that something is certain or expected not to happen as a result of action taken by an
agency the behaviour of which is outside the scope of the present document
might indicates a likelihood that something will happen as a result of action taken by some agency the
behaviour of which is outside the scope of the present document
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might not indicates a likelihood that something will not happen as a result of action taken by some agency
the behaviour of which is outside the scope of the present document
In addition:
is (or any other verb in the indicative mood) indicates a statement of fact
is not (or any other negative verb in the indicative mood) indicates a statement of fact
The constructions "is" and "is not" do not indicate requirements.
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1 Scope
The present document captures the findings of the study item, "Study on channel model for frequency spectrum above 6
GHz" [2] and from further findings of the study item, "Study on New Radio Access Technology [22]" and the study
item "Study on Channel Modeling for Indoor Industrial Scenarios [23]". The channel models in the present document
address the frequency range 0.5-100 GHz. The purpose of this TR is to help TSG RAN WG1 to properly model and
evaluate the performance of physical layer techniques using the appropriate channel model(s). Therefore, the TR will be
kept up-to-date via CRs in the future.
This document relates to the 3GPP evaluation methodology and covers the modelling of the physical layer of both
Mobile Equipment and Access Network of 3GPP systems.
This document is intended to capture the channel model(s) for frequencies from 0.5GHz up to 100GHz.
2 References
The following documents contain provisions which, through reference in this text, constitute provisions of the present
document.
- References are either specific (identified by date of publication, edition number, version number, etc.) or
non-specific.
- For a specific reference, subsequent revisions do not apply.
- For a non-specific reference, the latest version applies. In the case of a reference to a 3GPP document (including
a GSM document), a non-specific reference implicitly refers to the latest version of that document in the same
Release as the present document.
[1] 3GPP TR 21.905: "Vocabulary for 3GPP Specifications".
[2] 3GPP TD RP-151606: "Study on channel model for frequency spectrum above 6 GHz".
[3] 3GPP TR 36.873 (V12.2.0): "Study on 3D channel model for LTE".
[4] 3GPP RP-151847: "Report of RAN email discussion about >6GHz channel modelling", Samsung.
[5] 3GPP TD R1-163408: "Additional Considerations on Building Penetration Loss Modelling for 5G
System Performance Evaluation", Straight Path Communications.
[6] ICT-317669-METIS/D1.4: "METIS channel model, METIS 2020, Feb, 2015".
[7] Glassner, A S: "An introduction to ray tracing. Elsevier, 1989".
[8] McKown, J. W., Hamilton, R. L.: "Ray tracing as a design tool for radio networks, Network,
IEEE, 1991(6): 27-30".
[9] Kurner, T., Cichon, D. J., Wiesbeck, W.: "Concepts and results for 3D digital terrain-based wave
propagation models: An overview", IEEE J.Select. Areas Commun., vol. 11, pp. 1002–1012, 1993.
[10] Born, M., Wolf, E.: "Principles of optics: electromagnetic theory of propagation, interference and
diffraction of light", CUP Archive, 2000.
[11] Friis, H.: "A note on a simple transmission formula", proc. IRE, vol. 34, no. 5, pp. 254–256, 1946.
[12] Kouyoumjian, R.G., Pathak, P.H.: "A uniform geometrical theory of diffraction for an edge in a
perfectly conducting surface" Proc. IEEE, vol. 62, pp. 1448–1461, Nov. 1974.
[13] Pathak, P.H., Burnside, W., Marhefka, R.: "A Uniform GTD Analysis of the Diffraction of
Electromagnetic Waves by a Smooth Convex Surface", IEEE Transactions on Antennas and
Propagation, vol. 28, no. 5, pp. 631–642, 1980.
[14] IST-WINNER II Deliverable 1.1.2 v.1.2, "WINNER II Channel Models", IST-WINNER2, Tech.
Rep., 2007 (http://www.ist-winner.org/deliverables.html).
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[15] 3GPP TR36.101: "User Equipment (UE) radio transmission and reception".
[16] 3GPP TR36.104: "Base Station (BS) radio transmission and reception".
[17] Asplund, H., Medbo, J., Göransson, B., Karlsson, J., Sköld, J.: "A simplified approach to applying
the 3GPP spatial channel model", in Proc. of PIMRC 2006.
[18] ITU-R Rec. P.1816: "The prediction of the time and the spatial profile for broadband land mobile
services using UHF and SHF bands".
[19] ITU-R Rec. P.2040-1: "Effects of building materials and structures on radiowave propagation
above about 100 MHz", International Telecommunication Union Radiocommunication Sector
ITU-R, 07/2015.
[20] ITU-R Rec. P.527-3: "Electrical characteristics of the surface of the earth", International
Telecommunication Union Radiocommunication Sector ITU-R, 03/1992.
[21] Jordan, E.C., Balmain, K.G.: "Electromagnetic Waves and Radiating Systems", Prentice-Hall Inc.,
1968.
[22] 3GPP TD RP-162469: "Study on New Radio (NR) Access Technology".
[23] 3GPP TD RP-182138: "SID on Channel Modeling for Indoor Industrial Scenarios".
3 Definitions, symbols and abbreviations
3.1 Definitions
For the purposes of the present document, the terms and definitions given in TR 21.905 [1] apply.
3.2 Symbols
For the purposes of the present document, the following symbols apply:
A antenna radiation power pattern
Amax maximum attenuation
d2D 2D distance between Tx and Rx
d3D 3D distance between Tx and Rx
dH antenna element spacing in horizontal direction
dV antenna element spacing in vertical direction
f frequency
fc center frequency / carrier frequency
Frx,u,θ Receive antenna element u field pattern in the direction of the spherical basis vector ˆ
ˆ
Frx,u,ϕ Receive antenna element u field pattern in the direction of the spherical basis vector
Ftx,s,θ Transmit antenna element s field pattern in the direction of the spherical basis vector ˆ
ˆ
Frx,s,ϕ Transmit antenna element s field pattern in the direction of the spherical basis vector
hBS antenna height for BS
hUT antenna height for UT
rˆrx, n , m spherical unit vector of cluster n, ray m, for receiver
rˆtx, n , m spherical unit vector of cluster n, ray m, for transmitter
bearing angle
downtilt angle
slant angle
wavelength
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cross-polarization power ratio in linear scale
lgASA mean value of 10-base logarithm of azimuth angle spread of arrival
lgASD mean value of 10-base logarithm of azimuth angle spread of departure
lgDS mean value of 10-base logarithm of delay spread
lgZSA mean value of 10-base logarithm of zenith angle spread of arrival
lgZSD mean value of 10-base logarithm of zenith angle spread of departure
PrLOS LOS probability
SLAV side-lobe attenuation in vertical direction
lgASA standard deviation of 10-base logarithm of azimuth angle spread of arrival
lgASD standard deviation of 10-base logarithm of azimuth angle spread of departure
lgDS standard deviation value of 10-base logarithm of delay spread
lgZSA standard deviation of 10-base logarithm of zenith angle spread of arrival
lgZSD standard deviation of 10-base logarithm of zenith angle spread of departure
SF standard deviation of SF
azimuth angle
zenith angle
ˆ spherical basis vector (unit vector) for GCS
ˆ spherical basis vector (unit vector) for LCS
3dB horizontal 3 dB beamwidth of an antenna
ˆ spherical basis vector (unit vector), orthogonal to ˆ , for GCS
ˆ spherical basis vector (unit vector), orthogonal to ˆ , for LCS
etilt electrical steering angle in vertical direction
3dB vertical 3 dB beamwidth of an antenna
Angular displacement between two pairs of unit vectors
3.3 Abbreviations
For the purposes of the present document, the abbreviations given in TR 21.905 [1] and the following apply. An
abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in
TR 21.905 [1].
2D two-dimensional
3D three-dimensional
AOA Azimuth angle Of Arrival
AOD Azimuth angle Of Departure
AS Angular Spread
ASA Azimuth angle Spread of Arrival
ASD Azimuth angle Spread of Departure
BF Beamforming
BS Base Station
BP Breakpoint
BW Beamwidth
CDF Cumulative Distribution Function
CDL Clustered Delay Line
CRS Common Reference Signal
D2D Device-to-Device
DFT Discrete Fourier Transform
DS Delay Spread
GCS Global Coordinate System
IID Independent and identically distributed
InF Indoor Factory
InF-SL Indoor Factory with Sparse clutter and Low base station height (both Tx and Rx are below the
average height of the clutter)
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InF-DL Indoor Factory with Dense clutter and Low base station height (both Tx and Rx are below the
average height of the clutter)
InF-SH Indoor Factory with Sparse clutter and High base station height (Tx or Rx elevated above the
clutter)
InF-DH Indoor Factory with Dense clutter and High base station height (Tx or Rx elevated above the
clutter)
InF-HH Indoor Factory with High Tx and High Rx (both elevated above the clutter)
InH Indoor Hotspot
IRR Infrared Reflecting
ISD Intersite Distance
K Ricean K factor
LCS Local Coordinate System
LOS Line Of Sight
MIMO Multiple-Input-Multiple-Output
MPC Multipath Component
NLOS Non-LOS
O2I Outdoor-to-Indoor
O2O Outdoor-to-Outdoor
OFDM Orthogonal Frequency-Division Multiplexing
PAS Power angular spectrum
PL Path Loss
PRB Physical Resource Block
RCS Radar cross-section
RMa Rural Macro
RMS Root Mean Square
RSRP Reference Signal Received Power
Rx Receiver
SCM Spatial Channel Model
SINR Signal-to-Interference-plus-Noise Ratio
SIR Signal-to-Interference Ratio
SSCM Statistical Spatial Channel Model
SF Shadow Fading
SLA Sidelobe Attenuation
TDL Tapped Delay Line
TOA Time Of Arrival
TRP Transmission Reception Point
Tx Transmitter
UMa Urban Macro
UMi Urban Micro
UT User Terminal
UTD Uniform Theory of Diffraction
V2V Vehicle-to-Vehicle
XPR Cross-Polarization Ratio
ZOA Zenith angle Of Arrival
ZOD Zenith angle Of Departure
ZSA Zenith angle Spread of Arrival
ZSD Zenith angle Spread of Departure
4 Introduction
At TSG RAN #69 meeting the Study Item Description on "Study on channel model for frequency spectrum above 6
GHz" was approved [2]. This study item covers the identification of the status/expectation of existing information on
high frequencies (e.g. spectrum allocation, scenarios of interest, measurements, etc), and the channel model(s) for
frequencies up to 100 GHz. This technical report documents the channel model(s). The new channel model has to a
large degree been aligned with earlier channel models for <6 GHz such as the 3D SCM model (TR 36.873) or IMT-
Advanced (ITU-R M.2135). The new model supports comparisons across frequency bands over the range 0.5-100 GHz.
The modelling methods defined in this technical report are generally applicable over the range 0.5-100 GHz, unless
explicitly mentioned otherwise in this technical report for specific modelling method, involved parameters and/or
scenario.
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Subsequently, at the TSG RAN #81 meeting the Study Item Description "Study on Channel Modeling for Indoor
Industrial Scenarios" was approved [23]. The findings from this study item is also captured in the present technical
report. The Industrial channel model was developed by considering new measurements and information in the literature.
An overview list of all such contributions and sources is available in tdoc R1-1909706.
The channel model is applicable for link and system level simulations in the following conditions:
- For system level simulations, supported scenarios are urban microcell street canyon, urban macrocell, indoor
office, rural macrocell, and indoor factory.
- Bandwidth is supported up to 10% of the center frequency but no larger than 2GHz.
- Mobility of either one end of the link or both ends of the link is supported
- For the stochastic model, spatial consistency is supported by correlation of LSPs and SSPs as well as
LOS/NLOS state.
- Large array support is based on far field assumption and stationary channel over the size of the array.
5 Void
6 Status/expectation of existing information on high
frequencies
6.1 Channel modelling works outside of 3GPP
This clause summarizes the channel modelling work outside of 3GPP based on the input from companies.
Groups and projects with channel models:
- METIS (Mobile and wireless communications Enablers for the Twenty-twenty Information Society)
- MiWEBA (Millimetre-Wave Evolution for Backhaul and Access)
- ITU-R M
- COST2100
- IEEE 802.11
- NYU WIRELESS: interdisciplinary academic research center
- Fraunhofer HHI has developed the QuaDRiGa channel model, Matlab implementation is available at
http://quadriga-channel-model.de
Groups and projects which intend to develop channel models:
- 5G mmWave Channel Model Alliance: NIST initiated, North America based
- mmMAGIC (Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated
Communications): Europe based
- IMT-2020 5G promotion association: China based
METIS Channel Models:
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- Identified 5G requirements (e.g., wide frequency range, high bandwidth, massive MIMO, 3-D and accurate
polarization modelling)
- Performed channel measurements at various bands between 2GHz and 60 GHz
- Provided different channel model methodologies (map-based model, stochastic model or hybrid model). For
stochastic model, the proposed channel is focused on outdoor square, Indoor cafeteria and indoor shopping mall
scenarios.
MiWEBA Channel Models:
- Addressed various challenges: Shadowing, spatial consistency, environment dynamics, spherical wave
modelling, dual mobility Doppler model, ratio between diffuse and specular reflections, polarization
- Proposed Quasi-deterministic channel model
- Performed channel measurements at 60 GHz
- Focused on university campus, street canyon, hotel lobby, backhaul, and D2D scenarios.
ITU-R M Channel Models:
- Addressed the propagation loss and atmospheric loss on mmW
- Introduced enabling antenna array technology and semiconductor technology
- Proposed deployment scenarios, focused on dense urban environment for high data rate service: indoor shopping
mall, indoor enterprise, in home, urban hotspot in a square/street, mobility in city.
COST2100 and COST IC1004 Channel Models:
- Geometry-based stochastic channel model that reproduce the stochastic properties of MIMO channels over time,
frequency and space. It is a cluster-level model where the statistics of the large scale parameters are always
guaranteed in each series of channel instances.
NYU WIRELESS Channel Models:
- Conducted many urban propagation measurements on 28/38/60/73 GHz bands for both outdoor and indoor
channels, measurements are continuing.
- Proposed 3 areas for 5G mmWave channel modelling which are small modifications or extensions from 3GPP's
current below 6GHz channel models
- 1) LOS/NLOS/blockage modelling (a squared exponential term); 2). Wideband power delay profiles (time
clusters and spatial lobes for a simple extension to the existing 3GPP SSCM model); 3). Physics-based path loss
model (using the existing 3GPP path loss equations, but simply replacing the "floating" optimization parameter
with a deterministic 1 m "close-in" free space reference term in order to provide a standard and stable definition
of "path loss exponent" across all different parties, scenarios, and frequencies).
802.11 ad/ay Channel Models:
- Conducted ray-tracing methodology on 60 GHz band indoor channels, including conference room, cubicle,
living room scenarios
- Intra cluster parameters were proposed in terms of ray excess delay and ray power distribution
- Human blockage models were proposed in terms of blockage probability and blockage attenuation
5G mmWave Channel Model Alliance:
- Will provide a venue to promote fundamental research into measurement, analysis, identification of physical
parameters, and statistical representations of mmWave propagation channels.
- Divided into six collaborative working groups that include a Steering Committee; Modelling Methodology
Group; Measurement Methodology Group; and groups that focus on defining and parameterizing Indoor,
Outdoor, and Emerging Usage Scenarios.
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- Sponsored by Communications Technology Research Laboratory within the NIST.
mmMAGIC:
- Brings together major infrastructure vendors, major European operators, leading research institutes and
universities, measurement equipment vendors and one SME.
- Will undertake extensive radio channel measurements in the 6-100 GHz range.
- Will develop and validate advanced channel models that will be used for rigorous validation and feasibility
analysis of the proposed concepts and system, as well as for usage in regulatory and standards fora.
IMT-2020 5G promotion association
- Jointly established by three ministries of China based on the original IMT-Advanced promotion group
- Members including the main operators, vendors, universities and research institutes in China
- The major platform to promote 5G technology research in China and to facilitate international communication
and cooperation
QuaDRiGa (Fraunhofer HHI)
- QuaDRiGa (QUAsi Deterministic RadIo channel GenerAtor) was developed at the Fraunhofer Heinrich Hertz
Institute within the Wireless Communications and Networks Department to enable the modelling of MIMO radio
channels for specific network configurations, such as indoor, satellite or heterogeneous configurations.
- Besides being a fully-fledged 3D geometry-based stochastic channel model (well aligned with TR36.873),
QuaDRiGa contains a collection of features created in SCM(e) and WINNER channel models along with novel
modelling approaches which provide features to enable quasi-deterministic multi-link tracking of users (receiver)
movements in changing environments. QuaDRiGa supports Massive MIMO modelling enabled through a new
multi-bounce scattering approach and spherical wave propagation. It will be continuously extended with features
required by 5G and frequencies beyond 6 GHz. The QuaDRiGa model is supported by data from extensive
channel measurement campaigns at 10 / 28 / 43 / 60 / 82 GHz performed by the same group.
6.2 Scenarios of interest
Brief description of the key scenarios of interest identified (see note):
(1) UMi (Street canyon, open area) with O2O and O2I: This is similar to 3D-UMi scenario, where the BSs are
mounted below rooftop levels of surrounding buildings. UMi open area is intended to capture real-life scenarios
such as a city or station square. The width of the typical open area is in the order of 50 to 100 m.
Example: [Tx height:10m, Rx height: 1.5-2.5 m, ISD: 200m]
(2) UMa with O2O and O2I: This is similar to 3D-UMa scenario, where the BSs are mounted above rooftop levels
of surrounding buildings.
Example: [Tx height:25m, Rx height: 1.5-2.5 m, ISD: 500m]
(3) Indoor: This scenario is intended to capture various typical indoor deployment scenarios, including office
environments, and shopping malls. The typical office environment is comprised of open cubicle areas, walled
offices, open areas, corridors etc. The BSs are mounted at a height of 2-3 m either on the ceilings or walls. The
shopping malls are often 1-5 stories high and may include an open area (or "atrium") shared by several floors.
The BSs are mounted at a height of approximately 3 m on the walls or ceilings of the corridors and shops.
Example: [Tx height: 2-3m, Rx height: 1.5m, area: 500 square meters]
(4) Backhaul, including outdoor above roof top backhaul in urban area and street canyon scenario where small cell
BSs are placed at lamp posts.
(5) D2D/V2V. Device-to-device access in open area, street canyon, and indoor scenarios. V2V is a special case
where the devices are mobile.
(6) Other scenarios such as Stadium (open-roof) and Gym (close-roof).
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(7) Indoor industrial scenarios
Note: The scenarios of interest are based on the plenary email discussion and different from the supported
scenarios in clause 7. The indoor industrial scenarios were identified at a later stage in the TSG RAN #81
meeting.
6.3 Channel measurement capabilities
The measurement capability as reported by each company is summarized in the following table.
Table 6.3-1: Channel measurement capabilities
6 - 20 GHz 20 - 30 GHz 30 - 60 GHz >60 GHz
Urban macro CMCC Nokia/Aalborg NYU
Nokia/Aalborg
Urban micro Aalto University AT&T AT&T AT&T
CMCC Aalto University Huawei Aalto University
Ericsson CMCC Intel/Fraunhofer HHI Huawei
Intel/Fraunhofer HHI Huawei NTT DOCOMO Intel/Fraunhofer HHI
Nokia/Aalborg Intel/Fraunhofer HHI Qualcomm NYU
NTT DOCOMO Nokia/Aalborg CATT
Orange NTT DOCOMO ETRI
NYU ITRI/CCU
Qualcomm ZTE
Samsung
CATT
KT
ETRI
ITRI/CCU
ZTE
Indoor Aalto University AT&T AT&T AT&T
CMCC Alcatel-Lucent Ericsson Aalto University
Ericsson Aalto University Huawei Huawei
Huawei BUPT Intel/Fraunhofer HHI Intel/Fraunhofer HHI
Intel/Fraunhofer HHI CMCC NTT DOCOMO NYU
Nokia/Aalborg Huawei NYU
NTT DOCOMO Intel/Fraunhofer HHI Qualcomm
Orange Nokia/Aalborg CATT
NTT DOCOMO ETRI
NYU ITRI/CCU
Qualcomm ZTE
Samsung
CATT
KT
ETRI
ITRI/CCU
ZTE
O2I Ericsson AT&T AT&T AT&T
Huawei Alcatel-Lucent Ericsson Huawei
Intel/Fraunhofer HHI Ericsson Huawei Intel/Fraunhofer HHI
Nokia/Aalborg Huawei Intel/Fraunhofer HHI
NTT DOCOMO Intel/Fraunhofer HHI NTT DOCOMO
Orange NTT DOCOMO
NYU
Samsung
KT
6.4 Modelling objectives
The requirements for channel modelling are as follows.
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- Channel model SI should take into account the outcome of RAN-level discussion in the '5G' requirement study
item
- Complexity in terms of Description, Generating channel coefficients, development complexity and Simulation
time should be considered.
- Support frequency range up to 100 GHz.
- The critical path of the SI is 6 – 100 GHz
- Take care of mmW propagation aspects such as blocking and atmosphere attenuation.
- The model should be consistent in space, time and frequency
- Support large channel bandwidths (up to 10% of carrier frequency)
- Aim for the channel model to cover a range of coupling loss considering current typical cell sizes, e.g. up to km-
range macro cells. Note: This is to enable investigation of the relevance of the 5G system using higher frequency
bands to existing deployments.
- Accommodate UT mobility
- Mobile speed up to 500 km/h.
- Develop a methodology considering that model extensions to D2D and V2V may be developed in future SI.
- Support large antenna arrays
7 Channel model(s) for 0.5-100 GHz
7.1 Coordinate system
7.1.1 Definition
A coordinate system is defined by the x, y, z axes, the spherical angles and the spherical unit vectors as shown in Figure
7.1.1. Figure 7.1.1 defines the zenith angle and the azimuth angle in a Cartesian coordinate system. Note that
0 points to the zenith and 90 0 points to the horizon. The field component in the direction of ˆ is given by F
and the field component in the direction of ˆ is given by F .
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ˆ
n̂
ˆ
y
Figure 7.1.1: Definition of spherical angles and spherical unit vectors in a Cartesian coordinate
system, where n̂ is the given direction, ˆ and ˆ are the spherical basis vectors
7.1.2 Local and global coordinate systems
A Global Coordinate System (GCS) is defined for a system comprising multiple BSs and UTs. An array antenna for a
BS or a UT can be defined in a Local Coordinate System (LCS). An LCS is used as a reference to define the vector far-
field that is pattern and polarization, of each antenna element in an array. It is assumed that the far-field is known in the
LCS by formulae. The placement of an array within the GCS is defined by the translation between the GCS and a LCS.
The orientation of the array with respect to the GCS is defined in general by a sequence of rotations (described in clause
7.1.3). Since this orientation is in general different from the GCS orientation, it is necessary to map the vector fields of
the array elements from the LCS to the GCS. This mapping depends only on the orientation of the array and is given by
the equations in clause 7.1.3. Note that any arbitrary mechanical orientation of the array can be achieved by rotating the
LCS with respect to the GCS.
7.1.3 Transformation from a LCS to a GCS
A GCS with coordinates (x, y, z, , ) and unit vectors ( ˆ , ˆ ) and an LCS with "primed" coordinates (x', y', z', ' , ' )
and "primed" unit vectors ( ˆ' , ˆ' ) are defined with a common origins in Figures 7.1.3-1 and 7.1.3-2. Figure 7.1.3-1
illustrates the sequence of rotations that relate the GCS (gray) and the LCS (blue). Figure 7.1.3-2 shows the coordinate
direction and unit vectors of the GCS (gray) and the LCS (blue). Note that the vector fields of the array antenna
elements are defined in the LCS. In Figure 7.1.3-1 we consider an arbitrary 3D-rotation of the LCS with respect to the
GCS given by the angles , , . The set of angles , , can also be termed as the orientation of the array antenna with
respect to the GCS.
Note that the transformation from a LCS to a GCS depends only on the angles , , . The angle is called the bearing
angle, is called the downtilt angle and is called the slant angle.
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Figure 7.1.3-1: Orienting the LCS (blue) with Figure 7.1.3-2: Definition of spherical
respect to the GCS (gray) by a sequence of 3 coordinates and unit vectors in both the GCS
rotations: , , . and LCS.
Let A' ( ' , ' ) denote an antenna element pattern in the LCS and A( , ) denote the same antenna element pattern in the
GCS. Then the two are related simply by
A( , ) A' ( ' , ' ) (7.1-1)
with ' and ' given by (7.1-7) and (7.1-8).
Let us denote the polarized field components in the LCS by F' ( ' , ' ) , F' ( ' , ' ) and in the GCS by F ( , ) ,
F ( , ) . Then they are related by equation (7.1-11).
Any arbitrary 3D rotation can be specified by at most 3 elemental rotations, and following the framework of
Figure 7.1.3-1, a series of rotations about the z, y and x axes are assumed here, in that order. The dotted and double-
dotted marks indicate that the rotations are intrinsic, which means that they are the result of one () or two ()
intermediate rotations. In other words, the y axis is the original y axis after the first rotation about z, and the x axis is
the original x axis after the first rotation about z and the second rotation about y . A first rotation of about z sets the
antenna bearing angle (i.e. the sector pointing direction for a BS antenna element). The second rotation of about y
sets the antenna downtilt angle. Finally, the third rotation of about x sets the antenna slant angle. The orientation of
the x, y and z axes after all three rotations can be denoted as x , y and z
. These triple-dotted axes represents the final
orientation of the LCS, and for notational purposes denoted as the x', y' and z' axes (local or "primed" coordinate
system).
In order to establish the equations for transformation of the coordinate system and the polarized antenna field patterns
between the GCS and the LCS, it is necessary to determine the composite rotation matrix that describes the
transformation of point (x, y, z) in the GCS into point (x', y', z') in the LCS. This rotation matrix is computed as the
product of three elemental rotation matrices. The matrix to describe rotations about the z, y and x axes by the angles ,
and respectively and in that order is defined as
cos sin 0 cos 0 sin 1 0 0
R RZ RY RX sin cos 0 0 1 0 0 cos sin
0 1 sin 0 cos 0 sin cos
0
(7.1-2)
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The reverse transformation is given by the inverse of R, which is also equal to the transpose of R since it is orthogonal.
R 1 RX RY RZ RT (7.1-3)
The simplified forward and reverse composite rotation matrices are given by
cos cos cos sin sin sin cos cos sin cos sin sin
R sin cos sin sin sin cos cos sin sin cos cos sin
sin cos sin cos cos
(7.1-4)
and
cos cos sin cos sin
R cos sin sin sin cos
1
sin sin sin cos cos cos sin
cos sin cos sin sin sin sin cos cos sin cos cos
(7.1-5)
These transformations can be used to derive the angular and polarization relationships between the two coordinate
systems.
In order to establish the angular relationships, consider a point (x, y, z) on the unit sphere defined by the spherical
coordinates (=1, , ), where is the unit radius, is the zenith angle measured from the +z-axis, and is the azimuth
angle measured from the +x-axis in the x-y plane. The Cartesian representation of that point is given by
x sin cos
ˆ y sin sin
z cos
(7.1-6)
The zenith angle is computed as arccos ˆ ẑ and the azimuth angle as arg( xˆ ˆ j yˆ ˆ ) , where x̂ , ŷ and ẑ
are the Cartesian unit vectors. If this point represents a location in the GCS defined by and , the corresponding
position in the LCS is given by R
1
̂ , from which local angles ' and ' can be computed. The results are given in
equations (7.1-7) and (7.1-8).
0 T
' , , ; , arccos 0 R ˆ acoscos cos cos sin cos cos sin sin sin
1
1
(7.1-7)
1 T
cos sin cos sin cos (7.1-8)
' , , ; , arg j R ˆ arg
1
0
j cos sin cos sin sin cos cos sin sin
These formulae relate the spherical angles (, ) of the GCS to the spherical angles (', ') of the LCS given the rotation
operation defined by the angles ( , , ).
Let us denote the polarized field components F ( , ) , F ( , ) in the GCS and F' ( ' , ' ) , F' ( ' , ' ) in the LCS.
These are related by
F , ˆ , T Rˆ , ˆ , T Rˆ , F ,
F , ˆ , T Rˆ , ˆ , T Rˆ , F , (7.1-9)
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In this equation, ˆ and ˆ represent the spherical unit vectors of the GCS, and ˆ and ˆ are the representations in
the LCS. The forward rotation matrix R transforms the LCS unit vectors into the GCS frame of reference. These pairs
of unit vectors are orthogonal and can be represented as shown in Figure 7.1.3-3.
Figure 7.1.3-3: Rotation of the spherical basis vectors by an angle due to the orientation of the LCS
with respect to the GCS
Assuming an angular displacement of between the two pairs of unit vectors, the rotation matrix of equation (7.1-9)
can be further simplified as:
ˆ , T Rˆ , ˆ , T Rˆ , cos cos 2 cos sin
ˆ , T Rˆ , ˆ , T Rˆ , cos 2 cos
cos
sin (7.1-10)
and equation (7.1-9) can be written as:
F , cos sin F ,
F , sin cos F ,
(7.1-11)
The angle can be computed in numerous ways from equation (7.1-10), with one such way approach being
arg ˆ , T Rˆ , j ˆ , T Rˆ , (7.1-12)
The dot products are readily computed using the Cartesian representation of the spherical unit vectors. The general
expressions for these unit vectors are given by
cos cos
ˆ
cos sin
sin
(7.1-13)
and
sin
ˆ
cos
0
(7.1-14)
The angle can be expressed as a function of mechanical orientation ( , , ) and spherical position (, ), and is given
by
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sin cos sin cos cos sin sin cos cos
arg
j sin cos sin cos sin (7.1-15)
It can be shown that cos and sin can be expressed as:
cos cos sin sin cos cos sin sin cos
cos
1 cos cos cos sin cos cos sin sin sin (7.1-16)
2
sin cos sin sin cos
sin
1 cos cos cos sin cos cos sin sin sin (7.1-17)
2
7.1.4 Transformation from an LCS to a GCS for downtilt angle only
In this clause equations are provided for the transformation from LCS to GCS assuming that the orientation of the LCS
(with respect to the GCS) is such that the bearing angle =0, the downtilt angle is non-zero and the slant angle =0. In
other words the y'-axis of the LCS is parallel to the y-axis of the GCS. Considering a BS antenna element the x-axis of
the GCS is aligned with the pointing direction of the sector. Mechanical downtilt is modelled as a rotation of the LCS
around the y-axis. For zero mechanical downtilt the LCS coincides with the GCS.
This transformation relates the spherical angles ( , ) in the global coordinate system to spherical angles ( ' , ' ) in
the local (antenna-fixed) coordinate system and is defined as follows:
' arccoscos sin sin cos cos (7.1-18)
' argcos sin cos cos sin j sin sin (7.1-19)
where is the mechanical tilt angle around the y-axis as defined in Figure 7.1.4. Note that the equations (7.1-7), (7.1-
8) reduce to equations (7.1-18), (7.1-19) if both and are zero.
The antenna element pattern A( , ) in the GCS is related to the antenna element pattern A' ( ' , ' ) in the LCS by the
relation
A , A' ' , ' (7.1-20)
with ' and ' given by (7.1-18) and (7.1-19).
z
z'
' n̂
ˆ' ˆ
ˆ ' ˆ
n̂
ˆ'
ˆ'
y'
' ˆ
y
x
ˆ
x'
Figure 7.1.4: Definition of angles and unit vectors when the LCS has been rotated an angle around
the y-axis of the GCS
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For a mechanical tilt angle , the global coordinate system field components F ( , ) and F ( , ) , are calculated
from the field components F' ( ' , ' ) and F' ( ' , ' ) of the radiation pattern in the local (antenna-fixed) coordinate
system as:
F ( , ) F' ( ' , ' ) cos F' ( ' , ' ) sin (7.1-21)
F ( , ) F' ( ' , ' ) sin F' ( ' , ' ) cos (7.1-22)
where ' and ' are defined as in (7.1-18) and (7.1-19), and is defined as:
argsin cos cos cos sin j sin sin . (7.1-23)
Note that the equation (7.1-15) is reduced to equation (7.1-23) if both and are zero.
As an example, in the horizontal cut, i.e., for 90 , equations (7.1-18), (7.1-19) and (7.1-23) become
' arccoscos sin (7.1-24)
' argcos cos j sin (7.1-25)
argcos j sin sin (7.1-26)
7.2 Scenarios
The detailed scenario description in this clause can be used for channel model calibration.
UMi-street canyon and UMa
Details on UMi-street canyon and UMa scenarios are listed in Table 7.2-1.
Table 7.2-1: Evaluation parameters for UMi-street canyon and UMa scenarios
Parameters UMi - street canyon UMa
Hexagonal grid, 19 micro sites, 3 sectors Hexagonal grid, 19 macro sites, 3
Cell layout
per site (ISD = 200m) sectors per site (ISD = 500m)
BS antenna height hBS 10m 25m
Outdoor/indoor Outdoor and indoor Outdoor and indoor
UT LOS/NLOS LOS and NLOS LOS and NLOS
location
Height hUT Same as 3D-UMi in TR36.873 Same as 3D-UMa in TR36.873
Indoor UT ratio 80% 80%
UT mobility (horizontal plane
3km/h 3km/h
only)
Min. BS - UT distance (2D) 10m 35m
UT distribution (horizontal) Uniform Uniform
Indoor-office
Details on indoor-office scenarios are listed in Table 7.2-2 and presented in Figure 7.2-1. More details, if necessary, can
be added to Figure 7.2-1.
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Table 7.2-2: Evaluation parameters for indoor-office scenarios
Indoor - office Indoor - office
Parameters
open office mixed office
Room size
120mx50mx3m
Layout (WxLxH)
ISD 20m
BS antenna height hBS 3 m (ceiling)
LOS and NLOS
LOS/NLOS
UT location
Height hUT 1m
UT mobility (horizontal plane only) 3 km/h
Min. BS - UT distance (2D) 0
UT distribution (horizontal) Uniform
Note: The only difference between the open office and mixed office models in this TR is the line of sight
probability.
Figure 7.2-1: Layout of indoor office scenarios.
RMa
The rural deployment scenario focuses on larger and continuous coverage. The key characteristics of this scenario are
continuous wide area coverage supporting high speed vehicles. This scenario will be noise-limited and/or interference-
limited, using macro TRPs. Details of RMa scenario is described in Table 7.2-3.
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Table 7.2-3: Evaluation parameters for RMa
Parameters RMa
Carrier Frequency Up to 7Ghz
BS height hBS 35m
Layout Hexagonal grid, 19 Macro sites, 3sectors per site, ISD = 1732m or 5000m
UT height hUT 1.5m
UT distribution Uniform
Indoor/Outdoor 50% indoor and 50% in car
LOS/NLOS LOS and NLOS
Min BS - UT 35m
distance(2D)
Indoor Factory (InF)
The indoor factory (InF) scenario focuses on factory halls of varying sizes and with varying levels of density of
"clutter", e.g. machinery, assembly lines, storage shelves, etc. Details of the InF scenario are listed in Table 7.2-4.
Table 7.2-4: Evaluation parameters for InF
InF
InF-SL InF-DL InF-SH InF-DH InF-HH
Parameters (sparse clutter, (dense clutter, (sparse clutter, (dense clutter, (high Tx, high
low BS) low BS) high BS) high BS) Rx)
Rectangular: 20-160000 m2
Room size
Layout
Ceiling
5-25 m 5-15 m 5-25 m 5-15 m 5-25 m
height
Effective
clutter < Ceiling height, 0-10 m
height ℎ𝑐
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InF
InF-SL InF-DL InF-SH InF-DH InF-HH
Parameters (sparse clutter, (dense clutter, (sparse clutter, (dense clutter, (high Tx, high
low BS) low BS) high BS) high BS) Rx)
External
wall and Concrete or metal walls and ceiling with metal-coated windows
ceiling type
Small to medium Small to medium
Big machineries metallic Big machineries metallic
composed of machinery and composed of machinery and
regular metallic objects with regular metallic objects with
surfaces. irregular surfaces. irregular
For example: structure. For example: structure.
Clutter type several mixed For example: several mixed For example: Any
production assembly and production areas assembly and
areas with open production lines with open spaces production lines
spaces and surrounded by and surrounded by
storage/commis mixed small- storage/commissio mixed small-
sioning areas sized ning areas sized
machineries. machineries.
Typical clutter size,
10 m 2m 10 m 2m Any
𝑑𝑐𝑙𝑢𝑡𝑡𝑒𝑟
Clutter density 𝑟 Any
Low clutter High clutter High clutter
(percentage of surface Low clutter density
density density density
area occupied by (<40%)
(<40%) (≥40%) (≥40%)
clutter)
Clutter-embedded, i.e. the BS
BS antenna height antenna height is below the Above clutter Above clutter
average clutter height
LOS/NLOS LOS and NLOS 100% LOS
UT
location
Height Clutter-embedded Above clutter
7.3 Antenna modelling
This clause captures the antenna array structures considered in this SI for calibration.
The BS antenna is modelled by a uniform rectangular panel array, comprising MgNg panels, as illustrated in Figure 7.3-1
with Mg being the number of panels in a column and Ng being the number of panels in a row. Furthermore the following
properties apply:
- Antenna panels are uniformly spaced in the horizontal direction with a spacing of dg,H and in the vertical
direction with a spacing of dg,V.
- On each antenna panel, antenna elements are placed in the vertical and horizontal direction, where N is the
number of columns, M is the number of antenna elements with the same polarization in each column.
- Antenna numbering on the panel illustrated in Figure 7.3-1 assumes observation of the antenna array from
the front (with x-axis pointing towards broad-side and increasing y-coordinate for increasing column
number).
- The antenna elements are uniformly spaced in the horizontal direction with a spacing of dH and in the vertical
direction with a spacing of dV.
- The antenna panel is either single polarized (P =1) or dual polarized (P =2).
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The rectangular panel array antenna can be described by the following tuple M g , N g , M , N , P.
dg,H
……
(M-1,0) (M-1,1) (M-1,N-1)
……
……
……
……
dg,V
……
(1,0) (1,1) (1,N-1)
……
(0,0) (0,1) (0,N-1)
Figure 7.3-1: Cross-polarized panel array antenna model
The antenna radiation power pattern of each antenna element is generated according to Table 7.3-1.
Table 7.3-1: Radiation power pattern of a single antenna element
Parameter Values
90
2
, 0 min 12
AdB , SLAV
Vertical cut of the
3dB
radiation power pattern
with 3dB 65, SLAV 30 dB and 0, 180
(dB)
2
90, min 12
AdB , Amax
Horizontal cut of the
radiation power pattern 3dB
with 3dB 65, Amax 30 dB and - 180, 180
(dB)
3D radiation power ( , ) min AdB
AdB , 0 AdB
90, , Amax
pattern (dB)
Maximum directional gain
of an antenna element 8 dBi
GE,max
7.3.1 Antenna port mapping
Legacy BS array antennas, i.e. uniform linear arrays with fix phase shifts between its M elements to obtain a beamtilt in
vertical direction are modelled using complex weights
2
wm
1
exp j m 1dV cos etilt (7.3-1)
M
where m=1, …, M, etilt is the electrical vertical steering angle defined between 0° and 180° (90° represents
perpendicular to the array). denotes the wavelength and dV the vertical element spacing.
7.3.2 Polarized antenna modelling
In general the relationship between radiation field and power pattern is given by:
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A( , ) F , F ,
2 2
. (7.3-2)
The following two models represent two options on how to determine the radiation field patterns based on a defined
radiation power pattern.
Model-1:
In case of polarized antenna elements assume is the polarization slant angle where 0 degrees corresponds to a
purely vertically polarized antenna element and / 45 degrees correspond to a pair of cross-polarized antenna
elements. Then the antenna element field components in and direction are given by
F , cos sin F ,
F , sin cos F ,
(7.3-3)
cos sin sin sin cos sin cos
where cos , sin .
1 cos cos sin sin sin 1 cos cos sin sin sin
2 2
Note that the zenith and the azimuth field components F , , F , , F , and F , are
defined in terms of the spherical basis vectors of an LCS as defined in Clause 7.1. The difference between the single-
primed and the double-primed components is that the single-primed field components account for the polarization slant
and the double-primed field components do not. For a single polarized antenna (purely vertically polarized antenna) we
A , and F , 0 where A , is the 3D antenna radiation power
can write F ,
pattern as a function of azimuth angle and zenith angle in the LCS as defined in Table 7.3-1 converted into
linear scale.
Model-2:
In case of polarized antennas, the polarization is modelled as angle-independent in both azimuth and elevation, in an
LCS. For a linearly polarized antenna, the antenna element field pattern, in the vertical polarization and in the horizontal
polarization, are given by
F , A , cos (7.3-4)
and
F , A , sin , (7.3-5)
respectively, where is the polarization slant angle and A( , ) is the 3D antenna element power pattern as a
function of azimuth angle, and elevation angle, in the LCS. Note that 0 degrees correspond to a purely
vertically polarized antenna element. The vertical and horizontal field directions are defined in terms of the spherical
basis vectors, ˆ and ˆ respectively in the LCS as defined in Clause 7.1.2. Also A( , ) = A( , ) , =
and = as defined in Table 7.1-1.
7.4 Pathloss, LOS probability and penetration modelling
7.4.1 Pathloss
The pathloss models are summarized in Table 7.4.1-1 and the distance definitions are indicated in Figure 7.4.1-1 and
Figure 7.4.1-2. Note that the distribution of the shadow fading is log-normal, and its standard deviation for each
scenario is given in Table 7.4.1-1.
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d3
D-o
d3 ut
D
hBS hBS
d3
D-i
n
hUT hUT
d2D d2D-out d2D-in
Figure7.4.1-1: Definition of d2D and d3D Figure 7.4.1-2: Definition of d2D-out, d2D-in
for outdoor UTs and d3D-out, d3D-in for indoor UTs.
Note that
d3D out d3Din d2Dout d2Din 2 hBS hUT 2 (7.4-1)
Table 7.4.1-1: Pathloss models
LOS/NLOS
Scenario
Shadow Applicability range,
Pathloss [dB], fc is in GHz and d is in meters, see note 6 fading antenna height
std [dB] default values
PL 10m d 2D d BP hBS 35m
PLRMa LOS 1 , see note 5
PL2 d BP d 2D 10km hUT 1.5m
W 20m
LOS
PL1 20 log 10 (40d3D f c / 3) min( 0.03h1.72 ,10) log 10 (d3D ) SF 4 h 5m
min( 0.044h1.72 ,14.77) 0.002 log 10 (h)d3D h = avg. building height
PL2 PL1 (d BP ) 40 log 10 (d3D / d BP ) SF 6 W = avg. street width
RMa
The applicability ranges:
NLOS)
PLRMa NLOS max( PLRMa LOS, PLRMa 5m h 50m
for 10m d2D 5km 5m W 50m
SF 8
NLOS 161.04 7.1log 10 (W )+7.5 log 10 (h)
PLRMa 10m hBS 150m
NLOS
(24.37 3.7(h / hBS ) 2 ) log 10 (hBS ) 1m hUT 10m
(43.42 3.1 log 10 (hBS ))(log 10 (d 3D ) 3)
20 log 10 ( f c ) (3.2(log 10 (11.75hUT )) 2 4.97)
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LOS/NLOS
Scenario
Shadow Applicability range,
Pathloss [dB], fc is in GHz and d is in meters, see note 6 fading antenna height
std [dB] default values
PL 10m d 2D d BP
PLUMa LOS 1
d 2D 5km , see note 1
PL2 d BP
1.5m hUT 22.5m
LOS
PL1 28.0 22 log 10 (d 3D ) 20 log 10 ( f c ) SF 4 hBS 25m
PL2 28.0 40 log 10 (d 3D ) 20 log 10 ( f c )
) 2 (hBS hUT ) 2 )
9 log 10 (( d BP
UMa
NLOS)
PLUMa NLOS max( PLUMa LOS, PLUMa
1.5m hUT 22.5m
for 10m d2D 5km
hBS 25m
NLOS
NLOS 13.54 39.08 log 10 d 3D
PLUMa SF 6 Explanations: see note
3
20 log 10 f c 0.6(hUT 1.5)
Optional PL 32.4 20 log 10 f c 30 log 10 d 3D SF 7.8
PL 10m d 2D d BP
PLUMiLOS 1
d 2D 5km , see note 1
PL2 d BP
1.5m hUT 22.5m
SF 4
LOS
PL1 32.4 21log 10 (d3D ) 20 log 10 ( f c ) hBS 10m
UMi - Street Canyon
PL2 32.4 40 log 10 (d3D ) 20 log 10 ( f c )
) 2 (hBS hUT ) 2 )
9.5 log 10 (( d BP
PLUMiNLOS max( PLUMiLOS, PLUMi NLOS)
1.5m hUT 22.5m
for 10m d2D 5km
SF 7.82 hBS 10m
NLOS
NLOS 35.3 log 10 d3D 22.4
PLUMi Explanations: see note
4
21.3 log 10 f c 0.3(hUT 1.5)
Optional PL 32.4 20 log 10 f c 31.9 log 10 d3D SF 8.2
PLInHLOS 32.4 17.3 log 10 (d3D ) 20 log 10 ( f c ) SF 3 1m d3D 150m
LOS
InH - Office
PLInHNLOS max( PLInHLOS, PLInH NLOS)
SF 8.03 1m d3D 150m
NLOS 38.3 log 10 d3D 17.30 24.9 log 10 f c
NLOS
PLInH
Optional -NLOS 32.4 20 log 10 f c 31.9 log 10 d3D
PLInH SF 8.29 1m d3D 150m
LOS
InF
𝑃𝐿𝐿𝑂𝑆 = 31.84 + 21.50 log10 (𝑑3𝐷 ) + 19.00 log10 (𝑓𝑐 ) 𝜎𝑆𝐹 = 4.3 1 ≤ 𝑑3𝐷 ≤ 600 𝑚
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LOS/NLOS
Scenario
Shadow Applicability range,
Pathloss [dB], fc is in GHz and d is in meters, see note 6 fading antenna height
std [dB] default values
InF-SL: 𝑃𝐿 = 33 + 25.5 log10 (𝑑3𝐷 ) + 20 log10 (𝑓𝑐 )
𝜎𝑆𝐹 = 5.7
𝑃𝐿𝑁𝐿𝑂𝑆 = max(𝑃𝐿, 𝑃𝐿𝐿𝑂𝑆 )
InF-DL: 𝑃𝐿 = 18.6 + 35.7 log10 (𝑑3𝐷 ) + 20 log10 (𝑓𝑐 )
𝜎𝑆𝐹 = 7.2
NLOS
𝑃𝐿𝑁𝐿𝑂𝑆 = max(𝑃𝐿, 𝑃𝐿𝐿𝑂𝑆 , 𝑃𝐿𝐼𝑛𝐹−𝑆𝐿 )
InF-SH: 𝑃𝐿 = 32.4 + 23.0 log10 (𝑑3𝐷 ) + 20 log10 (𝑓𝑐 )
𝜎𝑆𝐹 = 5.9
𝑃𝐿𝑁𝐿𝑂𝑆 = max(𝑃𝐿, 𝑃𝐿𝐿𝑂𝑆 )
InF-DH: 𝑃𝐿 = 33.63 + 21.9 log10 (𝑑3𝐷 ) + 20 log10 (𝑓𝑐 )
𝜎𝑆𝐹 = 4.0
𝑃𝐿𝑁𝐿𝑂𝑆 = max(𝑃𝐿, 𝑃𝐿𝐿𝑂𝑆 )
Note 1: Breakpoint distance d'BP = 4 h'BS h'UT fc/c, where fc is the centre frequency in Hz, c = 3.0108 m/s is the
propagation velocity in free space, and h'BS and h'UT are the effective antenna heights at the BS and the UT,
respectively. The effective antenna heights h'BS and h'UT are computed as follows: h'BS = hBS – hE, h'UT = hUT – hE,
where hBS and hUT are the actual antenna heights, and hE is the effective environment height. For UMi hE = 1.0m.
For UMa hE=1m with a probability equal to 1/(1+C(d2D, hUT)) and chosen from a discrete uniform distribution
uniform(12,15,…,(hUT-1.5)) otherwise. With C(d2D, hUT) given by
0 , hUT 13m
C d 2D , hUT hUT 13
1.5
10 g d 2D ,13m hUT 23m
,
where
0 , d 2D 18m
g d 2D 5 d 2D d 2D
3
.
4 100 exp 150 ,18m d 2D
Note that hE depends on d2D and hUT and thus needs to be independently determined for every link between BS
sites and UTs. A BS site may be a single BS or multiple co-located BSs.
Note 2: The applicable frequency range of the PL formula in this table is 0.5 < fc < fH GHz, where fH = 30 GHz for RMa
and fH = 100 GHz for all the other scenarios. It is noted that RMa pathloss model for >7 GHz is validated based
on a single measurement campaign conducted at 24 GHz.
Note 3: UMa NLOS pathloss is from TR36.873 with simplified format and PL UMa-LOS = Pathloss of UMa LOS outdoor
scenario.
Note 4: PLUMi-LOS = Pathloss of UMi-Street Canyon LOS outdoor scenario.
Note 5: Break point distance dBP = 2π hBS hUT fc/c, where fc is the centre frequency in Hz, c = 3.0 108 m/s is the
propagation velocity in free space, and hBS and hUT are the antenna heights at the BS and the UT, respectively.
Note 6: fc denotes the center frequency normalized by 1GHz, all distance related values are normalized by 1m, unless it
is stated otherwise.
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7.4.2 LOS probability
The Line-Of-Sight (LOS) probabilities are given in Table 7.4.2-1.
Table 7.4.2-1 LOS probability
Scenario LOS probability (distance is in meters)
RMa
1 , d 2D-out 10m
PrLOS d 2D-out 10
exp ,10m d 2D-out
1000
UMi - Street 1 , d 2D-out 18m
canyon
PrLOS 18 d 18
exp 2D-out 1 ,18m d 2D-out
d 2D-out 36 d 2D- out
UMa 1 , d 2D-out 18m
18
3
PrLOS 18 d 5d d
exp 2D-out 1 1 C hUT 2D-out exp 2D-out ,18m d 2D-out
d 2D-out 63 d 2D-out 4 100 150
where
0 , hUT 13m
C (hUT ) hUT 13
1.5
10 ,13m hUT 23m
Indoor - Mixed
office 1 , d 2D-in 1.2m
d 1.2
PrLOS exp 2D-in ,1.2m d 2D-in 6.5m
4 .7
d 2D-in 6.5
exp 0.32 ,6.5m d 2D-in
32.6
Indoor - Open
office
1 , d 2D-in 5m
d 5
PrLOS exp 2D-in ,5m d 2D-in 49m
70.8
d 2D-in 49
exp 211.7 0.54 ,49m d 2D-in
InF-SL 𝑑2D
InF-SH PrLOS,subsce (𝑑2D ) = exp (− )
𝑘subsce
InF-DL
InF-DH where
𝑑𝑐𝑙𝑢𝑡𝑡𝑒𝑟
− for InF-SL and InF-DL
ln(1 − 𝑟)
𝑘𝑠𝑢𝑏𝑠𝑐𝑒 =
𝑑𝑐𝑙𝑢𝑡𝑡𝑒𝑟 ℎ𝐵𝑆 − ℎ𝑈𝑇
− ∙ for InF-SH and InF-DH
{ ln(1 − 𝑟) ℎ𝑐 − ℎ𝑈𝑇
The parameters 𝑑𝑐𝑙𝑢𝑡𝑡𝑒𝑟 , 𝑟, and ℎ𝑐 are defined in Table 7.2-4
InF-HH PrLOS = 1
Note: The LOS probability is derived with assuming antenna heights of 3m for indoor, 10m for UMi, and 25m
for UMa
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7.4.3 O2I penetration loss
7.4.3.1 O2I building penetration loss
The pathloss incorporating O2I building penetration loss is modelled as in the following:
PL PL b PL tw PL in N 0, P2 (7.4-2)
where PLb is the basic outdoor path loss given in Clause 7.4.1, where d 3D is replaced by d3Dout d3Din . PL tw is
the building penetration loss through the external wall, PLin is the inside loss dependent on the depth into the building,
and σP is the standard deviation for the penetration loss.
PL tw is characterized as:
Lmaterial_ i
N
PL tw PL npi 10 log 10 pi 10 10
i 1
(7.4-3)
PL npi is an additional loss is added to the external wall loss to account for non-perpendicular incidence;
Lmaterial_ i amaterial_ i bmaterial_ i f , is the penetration loss of material i, example values of which can be found in
N
Table 7.4.3-1; pi is proportion of i-th materials, where p i 1
i 1 ; and N is the number of materials.
Table 7.4.3-1: Material penetration losses
Material Penetration loss [dB]
Standard multi-pane glass Lglass 2 0.2 f
IRR glass LIIRglass 23 0.3 f
Concrete Lconcrete 5 4 f
Wood Lwood 4.85 0.12 f
Note: f is in GHz
Table 7.4.3-2 gives PL tw , PLin and σP for two O2I penetration loss models. The O2I penetration is UT-specifically
generated, and is added to the SF realization in the log domain.
Table 7.4.3-2: O2I building penetration loss model
Path loss through external wall: Indoor loss: Standard deviation:
PL tw in [dB] PLin in [dB] σP in [dB]
Lglass Lconcrete
Low-loss model
5 10 log 10 0.3 10 10
0.7 10 10 0.5 d 2Din 4.4
LIIRglass Lconcrete
High-loss model 5 10 log 10 0.7 10 10 0.3 10 10 0.5 d 2Din 6.5
d 2Din is minimum of two independently generated uniformly distributed variables between 0 and 25 m for UMa and
UMi-Street Canyon, and between 0 and 10 m for RMa. d 2Din shall be UT-specifically generated.
Both low-loss and high-loss models are applicable to UMa and UMi-Street Canyon.
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Only the low-loss model is applicable to RMa.
Only the high-loss model is applicable to InF.
The composition of low and high loss is a simulation parameter that should be determined by the user of the channel
models, and is dependent on the use of metal-coated glass in buildings and the deployment scenarios. Such use is
expected to differ in different markets and regions of the world and also may increase over years to new regulations and
energy saving initiatives. Furthermore, the use of such high-loss glass currently appears to be more predominant in
commercial buildings than in residential buildings in some regions of the world (see note).
Note: One example survey for the US market can be found in [5]. The survey does not necessarily be
representative for all the scenarios. Other ratios outside of the survey should not be precluded.
For backwards compatibility with TR 36.873 [3], the following building penetration model should be used for UMa and
UMi single-frequency simulations at frequencies below 6 GHz.
Table 7.4.3-3. O2I building penetration loss model for single-frequency simulations <6 GHz
Parameter Value
PL tw 20 dB
0.5 d 2Din
PLin with d 2Din being a single, link-specific, uniformly distributed variable between 0
and 25 m
P 0 dB
SF 7 dB (note: replacing the respective value in Table 7.4.1-1)
7.4.3.2 O2I car penetration loss
The pathloss incorporating O2I car penetration loss is modelled as in the following:
PL PL b Ν μ,σ P2 (7.4-4)
where PLb is the basic outdoor path loss given in Clause 7.4.1. μ = 9, and σP = 5. The car penetration loss shall be UT-
specifically generated. Optionally, for metallized car windows, μ = 20 can be used. The O2I car penetration loss models
are applicable for at least 0.6-60 GHz.
7.4.4 Autocorrelation of shadow fading
The long-term (log-normal) fading in the logarithmic scale around the mean path loss PL (dB) is characterized by a
Gaussian distribution with zero mean and standard deviation. Due to the slow fading process versus distance x
(x is in the horizontal plane), adjacent fading values are correlated. Its normalized autocorrelation function R(x) can
be described with sufficient accuracy by the exponential function ITU-R Rec. P.1816 [18]
x
Rx e d cor
(7.4-5)
with the correlation length dcor being dependent on the environment, see the correlation parameters for shadowing and
other large scale parameters in Table 7.5-6 (Channel model parameters). In a spatial consistency procedure in Clause
7.6.3, the cluster specific random variables are also correlated following the exponential function with respect to
correlation distances in the two dimensional horizontal plane.
7.5 Fast fading model
The radio channel realizations are created using the parameters listed in Table 7.5-1. The channel realizations are
obtained by a step-wise procedure illustrated in Figure 7.5-1 and described below. It has to be noted that the geometric
description covers arrival angles from the last bounce scatterers and respectively departure angles to the first scatterers
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interacted from the transmitting side. The propagation between the first and the last interaction is not defined. Thus, this
approach can model also multiple interactions with the scattering media. This indicates also that e.g., the delay of a
multipath component cannot be determined by the geometry. In the following steps, downlink is assumed. For uplink,
arrival and departure parameters have to be swapped.
Note: the channel generation in this clause is enough for at least the following cases.
- Case 1: For low complexity evaluations
- Case 2: To compare with earlier simulation results,
- Case 3: When none of the additional modelling components are turned on.
For other advanced simulations, e.g., spatially consistency, large bandwidth and arrays, oxygen absorption, blockage,
absolute time of arrival, dual mobility, embedded devices, etc., some of the additional modelling components of Clause
7.6 should be considered.
General parameters:
Set scenario, Assign propagation Generate correlated
network layout and condition (NLOS/ large scale
Calculate pathloss
antenna parameters LOS) parameters (DS,
AS, SF, K)
Small scale parameters:
Perform random Generate arrival & Generate cluster
Generate XPRs Generate delays
coupling of rays departure angles powers
Coefficient generation:
Draw random initial Generate channel Apply pathloss and
phases coefficient shadowing
Figure 7.5-1 Channel coefficient generation procedure
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ˆ
n̂
ˆ
y
x
Figure 7.5-2: Definition of a global coordinate system showing the zenith angle θ and the azimuth
angle ϕ. θ=0° points to zenith and θ=+90° points to the horizon.
The spherical basis vectors ˆ and ˆ shown above are defined based on the direction of propagation
n̂ .
Table 7.5-1: Notations in the global coordinate system (GCS)
Parameter Notation
LOS AOD ϕLOS,AOD
LOS AOA ϕLOS,AOA
LOS ZOD θLOS,ZOD
LOS ZOA θLOS,ZOA
AOA for cluster n ϕn,AOA
AOD for cluster n ϕn,AOD
AOA for ray m in cluster n ϕn,m,AOA
AOD for ray m in cluster n ϕn,m,AOD
ZOA for cluster n θn,ZOA
ZOD for cluster n θn,ZOD
ZOA for ray m in cluster n θn,m,ZOA
ZOD for ray m in cluster n θn,m,ZOD
Receive antenna element u field pattern in the direction of the spherical basis vector ˆ Frx,u,θ
Receive antenna element u field pattern in the direction of the spherical basis vector
ˆ Frx,u,ϕ
Transmit antenna element s field pattern in the direction of the spherical basis vector ˆ Ftx,s,θ
Transmit antenna element s field pattern in the direction of the spherical basis vector
ˆ Frx,s,ϕ
Step 1: Set environment, network layout, and antenna array parameters
a) Choose one of the scenarios (e.g. UMa, UMi-Street Canyon, RMa, InH-Office or InF). Choose a global
coordinate system and define zenith angle θ, azimuth angle ϕ, and spherical basis vectors ˆ , ˆ as shown in
Figure 7.3-2. Note: Scenario RMa is for up to 7GHz while others are for up to 100GHz
b) Give number of BS and UT
c) Give 3D locations of BS and UT, and determine LOS AOD (ϕLOS,AOD), LOS ZOD (θLOS,ZOD), LOS AOA
(ϕLOS,AOA), and LOS ZOA (θLOS,ZOA) of each BS and UT in the global coordinate system
d) Give BS and UT antenna field patterns Frx and Ftx in the global coordinate system and array geometries
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e) Give BS and UT array orientations with respect to the global coordinate system. BS array orientation is defined
by three angles ΩBS,α (BS bearing angle), ΩBS,β (BS downtilt angle) and ΩBS,γ (BS slant angle). UT array
orientation is defined by three angles ΩUT,α (UT bearing angle), ΩUT,β (UT downtilt angle) and ΩUT,γ (UT slant
angle).
f) Give speed and direction of motion of UT in the global coordinate system
g) Specify system centre frequency f c and bandwidth B
Note: In case wrapping is used, each wrapping copy of a BS or site should be treated as a separate BS/site
considering channel generation.
Large scale parameters:
Step 2: Assign propagation condition (LOS/NLOS) according to Table 7.4.2-1. The propagation conditions for different
BS-UT links are uncorrelated.
Also, assign an indoor/outdoor state for each UT. It is noted that all the links from a UT have the same indoor/outdoor
state.
Step 3: Calculate pathloss with formulas in Table 7.4.1-1 for each BS-UT link to be modelled.
Step 4: Generate large scale parameters, e.g. delay spread (DS), angular spreads (ASA, ASD, ZSA, ZSD), Ricean K
factor (K) and shadow fading (SF) taking into account cross correlation according to Table 7.5-6 and using the
procedure described in clause 3.3.1 of [14] with the square root matrix CMxM (0) being generated using the Cholesky
decomposition and the following order of the large scale parameter vector: sM = [sSF, sK, sDS, sASD, sASA, sZSD, sZSA]T.
These LSPs for different BS-UT links are uncorrelated, but the LSPs for links from co-sited sectors to a UT are the
same. In addition, these LSPs for the links of UTs on different floors are uncorrelated.
Limit random RMS azimuth arrival and azimuth departure spread values to 104 degrees, i.e., ASA= min(ASA, 104),
ASD = min(ASD, 104). Limit random RMS zenith arrival and zenith departure spread values to 52 degrees, i.e., ZSA =
min(ZSA, 52), ZSD = min(ZSD, 52).
Small scale parameters:
Step 5: Generate cluster delays n
Delays are drawn randomly from the delay distribution defined in Table 7.5-6. With exponential delay distribution
calculate
n r DS ln X n , (7.5-1)
Where r is the delay distribution proportionality factor, Xn ~ uniform(0,1), and cluster index n = 1,…,N. With uniform
delay distribution the delay values n are drawn from the corresponding range. Normalise the delays by subtracting the
minimum delay and sort the normalised delays to ascending order:
n sort n min n . (7.5-2)
In the case of LOS condition, additional scaling of delays is required to compensate for the effect of LOS peak addition
to the delay spread. The heuristically determined Ricean K-factor dependent scaling constant is
C 0.7705 0.0433K 0.0002 K 2 0.000017 K 3 , (7.5-3)
where K [dB] is the Ricean K-factor as generated in Step 4. The scaled delays
nLOS n / C , (7.5-4)
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are not to be used in cluster power generation.
Step 6: Generate cluster powers Pn .
Cluster powers are calculated assuming a single slope exponential power delay profile. Power assignment depends on
the delay distribution defined in Table 7.5-6. With exponential delay distribution the cluster powers are determined by
n
r 1
Pn exp n 10 10 (7.5-5)
r DS
where
Z n ~ N 0, 2 is the per cluster shadowing term in [dB]. Normalize the cluster powers so that the sum of all
cluster powers is equal to one, i.e.,
Pn
Pn (7.5-6)
P
N
n 1 n
In the case of LOS condition an additional specular component is added to the first cluster. Power of the single LOS ray
is:
KR
P1, LOS
KR 1 (7.5-7)
and the cluster powers are not normalized as in equation (7.5-6) , but:
Pn
n 1P1, LOS
1
Pn
K R 1 Pn
N
n 1 (7.5-8)
where (.) is Dirac's delta function and KR is the Ricean K-factor as generated in Step 4 converted to linear scale. These
power values are used only in equations (7.5-9) and (7.5-14), but not in equation (7.5-22).
Assign the power of each ray within a cluster as Pn / M, where M is the number of rays per cluster.
Remove clusters with less than -25 dB power compared to the maximum cluster power. The scaling factors need not be
changed after cluster elimination.
Step 7: Generate arrival angles and departure angles for both azimuth and elevation.
The composite PAS in azimuth of all clusters is modelled as wrapped Gaussian. The AOAs are determined by applying
the inverse Gaussian function (7.5-9) with input parameters Pn and RMS angle spread ASA
2(ASA / 1.4) ln Pn max Pn
n, AOA , (7.5-9)
C
with C defined as
C NLOS
C NLOS 1.1035 0.028K 0.002 K 2 0.0001K 3 , for LOS
, (7.5-10)
C , for NLOS
where CNLOS is defined as a scaling factor related to the total number of clusters and is given in Table 7.5-2:
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Table 7.5-2: Scaling factors for AOA, AOD generation
# clusters 4 5 8 10 11 12 14 15 16 19 20 25
NLOS
C 0.779 0.860 1.018 1.090 1.123 1.146 1.190 1.211 1.226 1.273 1.289 1.358
In the LOS case, constant C also depends on the Ricean K-factor K in [dB], as generated in Step 4. Additional scaling
of the angles is required to compensate for the effect of LOS peak addition to the angle spread.
Assign positive or negative sign to the angles by multiplying with a random variable Xn with uniform distribution to the
discrete set of {1,–1}, and add component Yn ~ N 0, ASA 7
2
to introduce random variation
n , AOA X nn, AOA Yn LOS , AOA , (7.5-11)
where ϕLOS,AOA is the LOS direction defined in the network layout description, see Step1c.
In the LOS case, substitute (7.5-11) by (7.5-12) to enforce the first cluster to the LOS direction ϕLOS, AOA
n, AOA X nn, AOA Yn X 11, AOA Y1 LOS , AOA (7.5-12)
Finally add offset angles m from Table 7.5-3 to the cluster angles
n ,m, AOA n, AOA c ASA m , (7.5-13)
where cASA is the cluster-wise rms azimuth spread of arrival angles (cluster ASA) in Table 7.5-6.
Table 7.5-3: Ray offset angles within a cluster, given for rms angle spread normalized to 1
Ray number m Basis vector of offset angles m
1,2 ± 0.0447
3,4 ± 0.1413
5,6 ± 0.2492
7,8 ± 0.3715
9,10 ± 0.5129
11,12 ± 0.6797
13,14 ± 0.8844
15,16 ± 1.1481
17,18 ± 1.5195
19,20 ± 2.1551
The generation of AOD ( n , m , AOD ) follows a procedure similar to AOA as described above.
The generation of ZOA assumes that the composite PAS in the zenith dimension of all clusters is Laplacian (see Table
7.5-6). The ZOAs are determined by applying the inverse Laplacian function (7.5-14) with input parameters Pn and
RMS angle spread ZSA
ZSA ln Pn max Pn
n,ZOA , (7.5-14)
C
with C defined as
C NLOS
C NLOS 1.3086 0.0339K 0.0077 K 2 0.0002K 3 , for LOS
, (7.5-15)
C , for NLOS
Where CNLOS is a scaling factor related to the total number of clusters and is given in Table 7.5-4:
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Table 7.5-4: Scaling factors for ZOA, ZOD generation
# clusters 8 10 11 12 15 19 20 25
NLOS
C 0.889 0.957 1.031 1.104 1.1088 1.184 1.178 1.282
In the LOS case, constant C also depends on the Ricean K-factor K in [dB], as generated in Step 4. Additional scaling
of the angles is required to compensate for the effect of LOS peak addition to the angle spread.
Assign positive or negative sign to the angles by multiplying with a random variable Xn with uniform distribution to the
discrete set of {1,–1}, and add component Yn ~ N 0, ZSA 7
2
to introduce random variation
n,ZOA X n n,ZOA Yn ZOA , (7.5-16)
where ZOA 90 if the BS-UT link is O2I and ZOA LOS ,ZOA otherwise. The LOS direction is defined in the
network layout description, see Step1c.
In the LOS case, substitute (7.5-16) by (7.5-17) to enforce the first cluster to the LOS direction θLOS,ZOA
n,ZOA X n n,ZOA Yn X 11,ZOA Y1 LOS ,ZOA . (7.5-17)
Finally add offset angles m from Table 7.5-3 to the cluster angles
n,m,ZOA n,ZOA cZSA m , (7.5-18)
where cZSA is the cluster-wise rms spread of ZOA (cluster ZSA) in Table 7.5-6. Assuming that n ,m , ZOA is wrapped
within [0, 360°], if n,m,ZOA [180,360] , then n ,m ,ZOA is set to (360 n,m,ZOA ) .
The generation of ZOD follows the same procedure as ZOA described above except equation (7.5-16) is replaced by
n,ZOD X n n,ZOD Yn LOS ,ZOD offset,ZOD , (7.5-19)
where variable Xn is with uniform distribution to the discrete set of {1,–1}, Yn ~ N 0, ZSD 7 , 2
offset, ZOD is given
in Tables 7.5-6/7/8 and equation (7.5-18) is replaced by
lgZSD
n,m,ZOD n,ZOD (3 / 8)(10 ) m (7.5-20)
where lgZSD is the mean of the ZSD log-normal distribution.
In the LOS case, the generation of ZOD follows the same procedure as ZOA described above using equation (7.5-17).
Step 8: Coupling of rays within a cluster for both azimuth and elevation
Couple randomly AOD angles n,m,AOD to AOA angles n,m,AOA within a cluster n, or within a sub-cluster in the case of
two strongest clusters (see Step 11 and Table 7.5-3). Couple randomly ZOD angles n ,m , ZOD with ZOA angles
n ,m ,ZOA using the same procedure. Couple randomly AOD angles n,m,AOD with ZOD angles n ,m ,ZOD within a cluster n
or within a sub-cluster in the case of two strongest clusters.
Step 9: Generate the cross polarization power ratios
Generate the cross polarization power ratios (XPR) for each ray m of each cluster n. XPR is log-Normal distributed.
Draw XPR values as
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n, m 10 X n ,m / 10
, (7.5-21)
where X n,m ~ N (XPR , XPR
2
) is Gaussian distributed with XPR and XPR from Table 7.5-6.
Note: X n , m is independently drawn for each ray and each cluster.
The outcome of Steps 1-9 shall be identical for all the links from co-sited sectors to a UT.
Coefficient generation:
Step 10: Draw initial random phases
Draw random initial phase
n,m , n,m , n,m , n,m for each ray m of each cluster n and for four different polarisation
combinations (θθ, θϕ, ϕθ, ϕϕ). The distribution for initial phases is uniform within (-).
Step 11: Generate channel coefficients for each cluster n and each receiver and transmitter element pair u, s.
The method described below is used at least for drop-based evaluations irrespective of UT speeds. Relevant cases for
drop-based evaluations are:
- Case 1: For low complexity evaluations
- Case 2: To compare with earlier simulation results,
- Case 3: When none of the additional modelling components are turned on.
- Case 4: When spatial consistency and/or blockage is modeled for MU-MIMO simulations
- Other cases are not precluded
For the N – 2 weakest clusters, say n = 3, 4,…, N, the channel coefficients are given by:
Pn M Frx,u , n ,m ,ZOA ,n ,m , AOA
T
exp j n ,m 1 exp j
n ,m
H NLOS
(t )
m 1 Frx,u , n , m , ZOA , n , m , AOA
n ,m
u , s ,n
M n ,m exp j
1
n ,m
exp j
n ,m
Ftx,s , n ,m ,ZOD ,n ,m , AOD
j 2 rˆrxT ,n ,m .d rx,u
exp
j 2 rˆtxT,n ,m .d tx,s rˆ T
.v
exp j 2 rx,n ,m t
F exp
tx , s , n , m , ZOD , n , m , AOD 0 0 0 (7.5-22)
where Frx,u,θ and Frx,u,ϕ are the field patterns of receive antenna element u according to (7.1-11) and in the direction of
the spherical basis vectors, ˆ and ˆ respectively, Ftx,s,θ and Ftx,s,ϕ are the field patterns of transmit antenna element s in
the direction of the spherical basis vectors, ˆ and ˆ respectively. Note that the patterns are given in the GCS and
therefore include transformations with respect to antenna orientation as described in Clause 7.1. rˆrx, n , m is the spherical
unit vector with azimuth arrival angle ϕn,m,AOA and elevation arrival angle θn,m,ZOA, given by
sin n ,m ,ZOA cos n ,m , AOA
rˆrx,n ,m sin n ,m ,ZOA sin n ,m , AOA
cos n ,m ,ZOA
, (7.5-23)
where n denotes a cluster and m denotes a ray within cluster n. rˆtx, n , m is the spherical unit vector with azimuth departure
angle ϕn,m,AOD and elevation departure angle θn,m,ZOD, given by
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sin n ,m ,ZOD cos n ,m , AOD
rˆtx,n ,m sin n ,m ,ZOD sin n ,m , AOD
cos n ,m ,ZOD
, (7.5-24)
where n denotes a cluster and m denotes a ray within cluster n. Also, d rx,u is the location vector of receive antenna
element u and d tx,s is the location vector of transmit antenna element s, n,m is the cross polarisation power ratio in
linear scale, and 0 is the wavelength of the carrier frequency. If polarisation is not considered, the 2x2 polarisation
matrix can be replaced by the scalar exp jn,m and only vertically polarised field patterns are applied.
The Doppler frequency component depends on the arrival angles (AOA, ZOA), and the UT velocity vector v with
speed v, travel azimuth angle ϕv, elevation angle θv and is given by
rˆrxT , n , m .v
vn , m , where v v.sin v cos v sin v sin v cos v .
T
(7.5-25)
0
For the two strongest clusters, say n = 1 and 2, rays are spread in delay to three sub-clusters (per cluster), with fixed
delay offset. The delays of the sub-clusters are
n,1 n
n, 2 n 1.28 cDS (7.5-26)
n,3 n 2.56 cDS
where cDS is cluster delay spread specified in Table 7.5-6. When intra-cluster delay spread is unspecified (i.e., N/A)
the value 3.91 ns is used; it is noted that this value results in the legacy behaviour with 5 and 10 ns sub-cluster delays.
Twenty rays of a cluster are mapped to sub-clusters as presented in Table 7.5-5 below. The corresponding offset angles
are taken from Table 7.5-3 with mapping of Table 7.5-5.
Table 7.5-5: Sub-cluster information for intra cluster delay spread clusters
sub-cluster # mapping to rays Power delay offset
i Ri Ri M n ,i n
i 1 R1 1,2,3,4,5,6,7,8,19,20 10/20 0
i2 R2 9,10,11,12,17,18 6/20 1.28 cDS
i 3 R3 13,14,15,16 4/20 2.56 cDS
Then, the channel impulse response is given by:
2 3 N
H uNLOS
,s ( , t ) H uNLOS
, s ,n ,m (t ) ( n ,i ) H u , s ,n (t ) ( n )
NLOS
(7.5-27)
n 1 i 1 mRi n 3
where H uNLOS NLOS
, s , n (t ) is given in (7.5-22) and H u , s ,n ,m (t ) defined as:
NLOS
(t )
Pn Frx,u , n , m, ZOA , n, m, AOA
exp j
T
n,m n, m 1 exp j
n,m
rx,u , n, m, ZOA , n , m, AOA
H F
u , s ,n,m
M n, m exp j
1
n,m
exp j
n,m
(7.5-28)
Ftx, s , n, m, ZOD , n, m, AOD rˆrxT , n, m .d rx,u rˆtxT, n , m .d tx, s rˆ T
.v
F
exp j 2
exp j 2 exp j 2 rx, n, m t
tx, s , n, m, ZOD , n, m, AOD 0 0 0
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In the LOS case, determine the LOS channel coefficient by:
Frx,u , LOS ,ZOA , LOS , AOA 1 0 Ftx,s , LOS ,ZOD , LOS , AOD
T
LOS
(t )
Frx,u , LOS ,ZOA , LOS , AOA 0 1 Ftx,s , LOS ,ZOD , LOS , AOD
H u ,s ,1
(7.5-29)
d 3D rˆrxT ,LOS .d rx,u rˆtxT,LOS .d tx,s rˆrxT ,LOS .v
exp j 2 exp j 2 exp j 2 exp j 2 t
0 0 0 0
where (.) is the Dirac's delta function and KR is the Ricean K-factor as generated in Step 4 converted to linear scale.
Then, the channel impulse response is given by adding the LOS channel coefficient to the NLOS channel impulse
response and scaling both terms according to the desired K-factor K R as
, s ( , t )
H uLOS
1
H uNLOS , t KR
, s ,1 (t ) 1 .
H uLOS (7.5-30)
KR 1 KR 1
,s
Step 12: Apply pathloss and shadowing for the channel coefficients.
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Table 7.5-6 Part-1: Channel model parameters for UMi-Street Canyon and UMa
UMi - Street Canyon UMa
Scenarios
LOS NLOS O2I LOS NLOS O2I
Delay spread (DS) lgDS -0.24 log10(1+ fc) - 7.14 -0.24 log10(1+ fc) - 6.83 -6.62 -6.955 - 0.0963 log10(fc) -6.28 - 0.204 log10(fc) -6.62
lgDS=log10(DS/1s) lgDS 0.38 0.16 log10(1+ fc) + 0.28 0.32 0.66 0.39 0.32
AOD spread (ASD) lgASD -0.05 log10(1+ fc) + 1.21 -0.23 log10(1+ fc) + 1.53 1.25 1.06 + 0.1114 log10(fc) 1.5 - 0.1144 log10(fc) 1.25
lgASD=log10(ASD/1) lgASD 0.41 0.11 log10(1+ fc) + 0.33 0.42 0.28 0.28 0.42
AOA spread (ASA) lgASA -0.08 log10(1+ fc) + 1.73 -0.08 log10(1+ fc) + 1.81 1.76 1.81 2.08 - 0.27 log10(fc) 1.76
lgASA=log10(ASA/1) lgASA 0.014 log10(1+ fc) + 0.28 0.05 log10(1+ fc) + 0.3 0.16 0.20 0.11 0.16
ZOA spread (ZSA) lgZSA -0.1 log10(1+ fc) + 0.73 -0.04 log10(1+ fc) + 0.92 1.01 0.95 -0.3236 log10(fc) + 1.512 1.01
lgZSA=log10(ZSA/1) lgZSA -0.04 log10(1+ fc) + 0.34 -0.07 log10(1+ fc) + 0.41 0.43 0.16 0.16 0.43
Shadow fading (SF) [dB] SF See Table 7.4.1-1 See Table 7.4.1-1 7 See Table 7.4.1-1 See Table 7.4.1-1 7
K 9 N/A N/A 9 N/A N/A
K-factor (K) [dB]
K 5 N/A N/A 3.5 N/A N/A
ASD vs DS 0.5 0 0.4 0.4 0.4 0.4
ASA vs DS 0.8 0.4 0.4 0.8 0.6 0.4
ASA vs SF -0.4 -0.4 0 -0.5 0 0
ASD vs SF -0.5 0 0.2 -0.5 -0.6 0.2
DS vs SF -0.4 -0.7 -0.5 -0.4 -0.4 -0.5
Cross-Correlations ASD vs ASA 0.4 0 0 0 0.4 0
ASD vs -0.2 N/A N/A 0 N/A N/A
ASA vs -0.3 N/A N/A -0.2 N/A N/A
DS vs -0.7 N/A N/A -0.4 N/A N/A
SF vs 0.5 N/A N/A 0 N/A N/A
ZSD vs SF 0 0 0 0 0 0
ZSA vs SF 0 0 0 -0.8 -0.4 0
ZSD vs K 0 N/A N/A 0 N/A N/A
ZSA vs K 0 N/A N/A 0 N/A N/A
ZSD vs DS 0 -0.5 -0.6 -0.2 -0.5 -0.6
Cross-Correlations 1) ZSA vs DS 0.2 0 -0.2 0 0 -0.2
ZSD vs ASD 0.5 0.5 -0.2 0.5 0.5 -0.2
ZSA vs ASD 0.3 0.5 0 0 -0.1 0
ZSD vs ASA 0 0 0 -0.3 0 0
ZSA vs ASA 0 0.2 0.5 0.4 0 0.5
ZSD vs ZSA 0 0 0.5 0 0 0.5
Delay scaling parameter r 3 2.1 2.2 2.5 2.3 2.2
XPR 9 8.0 9 8 7 9
XPR [dB]
XPR 3 3 5 4 3 5
Number of clusters N 12 19 12 12 20 12
Number of rays per cluster M 20 20 20 20 20 20
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UMi - Street Canyon UMa
Scenarios
LOS NLOS O2I LOS NLOS O2I
max(0.25, 6.5622 max(0.25, 6.5622
Cluster DS ( cDS ) in [ns] 5 11 11 11
-3.4084 log10(fc)) -3.4084 log10(fc))
Cluster ASD ( c ASD ) in [deg] 3 10 5 5 2 5
Cluster ASA ( c ASA ) in [deg] 17 22 8 11 15 8
Cluster ZSA ( cZSA ) in [deg] 7 7 3 7 7 3
Per cluster shadowing std [dB] 3 3 4 3 3 4
DS 7 10 10 30 40 10
ASD 8 10 11 18 50 11
ASA 8 9 17 15 50 17
Correlation distance in
SF 10 13 7 37 50 7
the horizontal plane [m]
15 N/A N/A 12 N/A N/A
ZSA 12 10 25 15 50 25
ZSD 12 10 25 15 50 25
fc is carrier frequency in GHz; d2D is BS-UT distance in km.
NOTE 1: DS = rms delay spread, ASD = rms azimuth spread of departure angles, ASA = rms azimuth spread of arrival angles, ZSD = rms zenith spread of departure angles,
ZSA = rms zenith spread of arrival angles, SF = shadow fading, and K = Ricean K-factor.
NOTE 2: The sign of the shadow fading is defined so that positive SF means more received power at UT than predicted by the path loss model.
NOTE 3: All large scale parameters are assumed to have no correlation between different floors.
NOTE 4: The following notation for mean (μlgX=mean{log10(X) }) and standard deviation (σlgX=std{log10(X) }) is used for logarithmized parameters X.
NOTE 5: For all considered scenarios the AOD/AOA distributions are modelled by a wrapped Gaussian distribution, the ZOD/ZOA distributions are modelled by a Laplacian
distribution and the delay distribution is modelled by an exponential distribution.
NOTE 6: For UMa and frequencies below 6 GHz, use fc = 6 when determining the values of the frequency-dependent LSP values
NOTE 7: For UMi and frequencies below 2 GHz, use fc = 2 when determining the values of the frequency-dependent LSP values
Table 7.5-6 Part-2: Channel model parameters for RMa (up to 7GHz) and Indoor-Office
RMa Indoor-Office
Scenarios
LOS NLOS O2I LOS NLOS
Delay spread (DS) lgDS -7.49 -7.43 -7.47 -0.01 log10(1+fc) - 7.692 -0.28 log10(1+fc) - 7.173
lgDS=log10(DS/1s) lgDS 0.55 0.48 0.24 0.18 0.10 log10(1+fc) + 0.055
AOD spread (ASD) lgASD 0.90 0.95 0.67 1.60 1.62
lgASD=log10(ASD/1) lgASD 0.38 0.45 0.18 0.18 0.25
AOA spread (ASA) lgASA 1.52 1.52 1.66 -0.19 log10(1+fc) + 1.781 -0.11 log10(1+fc) + 1.863
lgASA=log10(ASA/1) lgASA 0.24 0.13 0.21 0.12 log10(1+fc) + 0.119 0.12 log10(1+fc) + 0.059
ZOA spread (ZSA) lgZSA 0.47 0.58 0.93 -0.26 log10(1+fc) + 1.44 -0.15 log10(1+fc) + 1.387
lgZSA=log10(ZSA/1) lgZSA 0.40 0.37 0.22 -0.04 log10(1+fc) + 0.264 -0.09 log10(1+fc) + 0.746
Shadow fading (SF) [dB] SF See Table 7.4.1-1 8 See Table 7.4.1-1
K-factor (K) [dB] K 7 N/A N/A 7 N/A
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RMa Indoor-Office
Scenarios
LOS NLOS O2I LOS NLOS
K 4 N/A N/A 4 N/A
ASD vs DS 0 -0.4 0 0.6 0.4
ASA vs DS 0 0 0 0.8 0
ASA vs SF 0 0 0 –0.5 –0.4
ASD vs SF 0 0.6 0 –0.4 0
DS vs SF -0.5 -0.5 0 –0.8 –0.5
Cross-Correlations ASD vs ASA 0 0 -0.7 0.4 0
ASD vs 0 N/A N/A 0 N/A
ASA vs 0 N/A N/A 0 N/A
DS vs 0 N/A N/A -0.5 N/A
SF vs 0 N/A N/A 0.5 N/A
ZSD vs SF 0.01 -0.04 0 0.2 0
ZSA vs SF -0.17 -0.25 0 0.3 0
ZSD vs K 0 N/A N/A 0 N/A
ZSA vs K -0.02 N/A N/A 0.1 N/A
ZSD vs DS -0.05 -0.10 0 0.1 -0.27
Cross-Correlations 1) ZSA vs DS 0.27 -0.40 0 0.2 -0.06
ZSD vs ASD 0.73 0.42 0.66 0.5 0.35
ZSA vs ASD -0.14 -0.27 0.47 0 0.23
ZSD vs ASA -0.20 -0.18 -0.55 0 -0.08
ZSA vs ASA 0.24 0.26 -0.22 0.5 0.43
ZSD vs ZSA -0.07 -0.27 0 0 0.42
Delay scaling parameter r 3.8 1.7 1.7 3.6 3
XPR 12 7 7 11 10
XPR [dB]
XPR 4 3 3 4 4
Number of clusters N 11 10 10 15 19
Number of rays per cluster M 20 20 20 20 20
Cluster DS ( cDS ) in [ns] N/A N/A N/A N/A N/A
Cluster ASD ( c ASD ) in [deg] 2 2 2 5 5
Cluster ASA ( c ASA ) in [deg] 3 3 3 8 11
Cluster ZSA ( cZSA ) in [deg] 3 3 3 9 9
Per cluster shadowing std [dB] 3 3 3 6 3
DS 50 36 36 8 5
ASD 25 30 30 7 3
Correlation distance in the ASA 35 40 40 5 3
horizontal plane [m] SF 37 120 120 10 6
40 N/A N/A 4 N/A
ZSA 15 50 50 4 4
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RMa Indoor-Office
Scenarios
LOS NLOS O2I LOS NLOS
ZSD 15 50 50 4 4
fc is carrier frequency in GHz; d2D is BS-UT distance in km.
NOTE 1: DS = rms delay spread, ASD = rms azimuth spread of departure angles, ASA = rms azimuth spread of arrival angles, ZSD = rms zenith spread of departure angles, ZSA =
rms zenith spread of arrival angles, SF = shadow fading, and K = Ricean K-factor.
NOTE 2: The sign of the shadow fading is defined so that positive SF means more received power at UT than predicted by the path loss model.
NOTE 3: The following notation for mean (μlgX=mean{log10(X) }) and standard deviation (σlgX=std{log10(X) }) is used for logarithmized parameters X.
NOTE 4: Void.
NOTE 5: For all considered scenarios the AOD/AOA distributions are modelled by a wrapped Gaussian distribution, the ZOD/ZOA distributions are modelled by a Laplacian
distribution and the delay distribution is modelled by an exponential distribution.
NOTE 6: For InH and frequencies below 6 GHz, use fc = 6 when determining the values of the frequency-dependent LSP values
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Table 7.5-6 Part-3: Channel model parameters for InF
InF
Scenarios
LOS NLOS
log10(26(V/S)+14)-9.35 log10(30(V/S)+32)-9.44
Delay spread (DS) lgDS
lgDS=log10(DS/1s) 4)
lgDS 0.15 0.19
AOD spread (ASD) lgASD 1.56 1.57
lgASD=log10(ASD/1) lgASD 0.25 0.2
AOA spread (ASA) lgASA -0.18*log10(1+fc) + 1.78 1.72
lgASA=log10(ASA/1) lgASA 0.12*log10(1+fc) + 0.2 0.3
ZOA spread (ZSA) lgZSA -0.2*log10(1+fc) + 1.5 -0.13*log10(1+fc) + 1.45
lgZSA=log10(ZSA/1) lgZSA 0.35 0.45
Shadow fading (SF)
SF Specified as part of path loss models
[dB]
K 7 N/A
K-factor (K) [dB]
K 8 N/A
ASD vs DS 0 0
ASA vs DS 0 0
ASA vs SF 0 0
ASD vs SF 0 0
DS vs SF 0 0
Cross-Correlations ASD vs ASA 0 0
ASD vs -0.5 N/A
ASA vs 0 N/A
DS vs -0.7 N/A
SF vs 0 N/A
ZSD vs SF 0 0
ZSA vs SF 0 0
ZSD vs K 0 N/A
ZSA vs K 0 N/A
ZSD vs DS 0 0
Cross-Correlations 1) ZSA vs DS 0 0
ZSD vs ASD 0 0
ZSA vs ASD 0 0
ZSD vs ASA 0 0
ZSA vs ASA 0 0
ZSD vs ZSA 0 0
Delay scaling parameter r 2.7 3
XPR 12 11
XPR [dB]
XPR 6 6
Number of clusters N 25 25
Number of rays per cluster M 20 20
c
Cluster DS ( DS ) in [ns] N/A N/A
c
Cluster ASD ( ASD ) in [deg] 5 5
c
Cluster ASA ( ASA ) in [deg] 8 8
c
Cluster ZSA ( ZSA ) in [deg] 9 9
Per cluster shadowing std [dB] 4 3
DS 10 10
ASD 10 10
Correlation distance ASA 10 10
in the horizontal SF 10 10
plane [m] 5) 10 N/A
ZSA 10 10
ZSD 10 10
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fc is carrier frequency in GHz; d2D is BS-UT distance in km.
NOTE 1: DS = rms delay spread, ASD = rms azimuth spread of departure angles, ASA = rms azimuth spread of arrival
angles, ZSD = rms zenith spread of departure angles, ZSA = rms zenith spread of arrival angles, SF = shadow
fading, and K = Ricean K-factor.
NOTE 2: The sign of the shadow fading is defined so that positive SF means more received power at UT than predicted
by the path loss model.
NOTE 3: The following notation for mean (μlgX=mean{log10(X) }) and standard deviation (σlgX=std{log10(X) }) is used for
logarithmized parameters X.
NOTE 4: V = hall volume in m3, S = total surface area of hall in m2 (walls+floor+ceiling)
NOTE 5: When the simulation scenario is small it is recommended to consider methods to improve the statistical
confidence, such as using multiple random seeds in simulations.
Table 7.5-7: ZSD and ZOD offset parameters for UMa
Scenarios LOS NLOS
max[-0.5, -2.1(d2D/1000) max[-0.5, -2.1(d2D/1000)
ZOD spread (ZSD) lgZSD -0.01 (hUT - 1.5)+0.75] -0.01(hUT - 1.5)+0.9]
lgZSD=log10(ZSD/1)
lgZSD 0.40 0.49
e(fc)-10^{a(fc) log10(max(b(fc), d2D))
ZOD offset µoffset,ZOD 0
+c(fc) -0.07(hUT-1.5)}
Note: For NLOS ZOD offset:
a(fc) = 0.208log10(fc)- 0.782;
b(fc) = 25;
c(fc) = -0.13log10(fc)+2.03;
e(fc) = 7.66log10(fc)-5.96.
Table 7.5-8: ZSD and ZOD offset parameters for UMi – Street Canyon
Scenarios LOS NLOS
max[-0.21, -14.8(d2D/1000) max[-0.5, -3.1(d2D/1000)
ZOD spread (ZSD) lgZSD + 0.01|hUT-hBS| + 0.83] + 0.01 max(hUT-hBS,0) +0.2]
lgZSD=log10(ZSD/1)
lgZSD 0.35 0.35
ZOD offset µoffset,ZOD 0 -10^{-1.5log10(max(10, d2D))+3.3}
Table 7.5-9: ZSD and ZOD offset parameters for RMa
Scenarios LOS NLOS O2I
max[-1, -0.17(d2D/1000) max[-1, -0.19(d2D/1000) max[-1, -0.19(d2D/1000)
ZOD spread (ZSD) lgZSD -0.01(hUT - 1.5) + 0.22] -0.01(hUT - 1.5) + 0.28] -0.01(hUT - 1.5) + 0.28]
lgZSD=log10(ZSD/1)
lgZSD 0.34 0.30 0.30
arctan((35 - 3.5)/d2D) arctan((35 - 3.5)/d2D)
ZOD offset µoffset,ZOD 0
-arctan((35 - 1.5)/d2D) -arctan((35 - 1.5)/d2D)
Table 7.5-10: ZSD and ZOD offset parameters for Indoor-Office
Scenarios LOS NLOS
ZOD spread (ZSD) lgZSD -1.43 log10(1+ fc)+2.228 1.08
lgZSD=log10(ZSD/1) lgZSD 0.13 log10(1+fc)+0.30 0.36
ZOD offset µoffset,ZOD 0 0
Table 7.5-11: ZSD and ZOD offset parameters for InF
Scenarios LOS NLOS
ZOD spread (ZSD) lgZSD 1.35 1.2
lgZSD=log10(ZSD/1) lgZSD 0.35 0.55
ZOD offset µoffset,ZOD 0 0
Notes for Tables 7.5-7, 7.5-8, 7.5-9, 7.5-10, 7.5-11:
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NOTE 1: fc is center frequency in GHz; d2D is BS-UT distance in m.
NOTE 2: hBS and hUT are antenna heights in m for BS and UT respectively.
NOTE 3: The following notation for mean (μlgX=mean{log10(X) }) and standard deviation (σlgX=std{log10(X) }) is
used for logarithmized parameters X.
NOTE 4: For frequencies below 6 GHz, use fc = 6 when determining the values of the frequency-dependent ZSD
and ZOD offset parameters in Table 7.5-7 and 7.5-10
NOTE 5: The ZSD parameters for O2I links are the same parameters that are used for outdoor links, depending on
the LOS condition of the outdoor link part in Tables 7.5-7 and 7.5-8.
7.6 Additional modelling components
The additional modelling components in this clause are to support advanced simulations, such as simulations with very
large arrays and large bandwidth, simulations affected by oxygen absorption (frequencies between 53 and 67 GHz),
simulations in which spatial consistency is important (e.g. high number of closely located user), simulations of mobility,
simulations of blockage effects, simulations of absolute time of arrival, simulations of dual mobility, simulations that
include sources of EM interference, and simulations of devices embedded in machinery or enclosures. These modelling
components affect some of the steps between Step 1 and Step 12 in Clause 7.5.
These extensions are computationally more expensive and might not be required in all evaluation cases.
7.6.1 Oxygen absorption
Oxygen absorption loss is applied to the cluster responses generated in Step 11 in Clause 7.5. The additional loss,
OLn(fc) for cluster n at centre frequency fc is modelled as:
( fc )
OLn ( f c ) (d 3 D c ( n )) [dB] (7.6-1)
1000
where:
- α(fc) is frequency dependent oxygen loss [dB/km] characterized in Table 7.6.1-1;
- c is the speed of light [m/s]; and d3D is the distance [m];
- τn is the n-th cluster delay [s] in Step 11 in Clause 7.5;
- τΔ is 0 in the LOS case and min( n ) otherwise, where min( n ) is the minimum delay in Step 5.
For centre frequencies not specified in this table, the frequency dependent oxygen loss α(fc) is obtained from a linear
interpolation between two loss values corresponding to the two adjacent centre frequencies of the centre frequency fc.
Table 7.6.1-1 Frequency dependent oxygen loss α(f) [dB/km]
f in [GHz] 0-52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68-100
α(f) in [dB/km] 0 1 2.2 4 6.6 9.7 12.6 14.6 15 14.6 14.3 10.5 6.8 3.9 1.9 1 0
For large channel bandwidth, first transform the time-domain channel response of each cluster (all rays within one
cluster share common oxygen absorption loss for simplicity) into frequency-domain channel response, and apply the
oxygen absorption loss to the cluster's frequency-domain channel response for frequency fc + Δf within the considered
bandwidth. The oxygen loss, OLn(fc+ Δf) for cluster n at frequency fc+ Δf is modelled as:
( f c f )
OLn ( f c f ) (d3 D c ( n ))
1000 [dB] (7.6-2)
where:
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- α(fc+ Δf) is the oxygen loss (dB/km) at frequency fc+ Δf characterized in Table 7.6.1-1. Note that Δf is in [-B/2,
B/2], where B is the bandwidth. Linear interpolation is applied for frequencies not provided in Table 7.6.1-1.
The final frequency-domain channel response is obtained by the summation of frequency-domain channel responses of
all clusters.
Time-domain channel response is obtained by the reverse transform from the obtained frequency-domain channel
response.
7.6.2 Large bandwidth and large antenna array
7.6.2.1 Modelling of the propagation delay
The modelling in this clause applies only when the bandwidth B is greater than c/D Hz, where
- D is the maximum antenna aperture in either azimuth or elevation (m)
- c is the speed of light (m/s)
Each ray within a cluster for a given u (Rx) and s (Tx) has unique time of arrival (TOA). The channel coefficient
generation step (Step 11 in Clause 7.5) is updated to model individual rays. In this case, the channel response of ray m
in cluster n for a link between Rx antenna u and Tx antenna s at delay at time t is given by:
NLOS Frx,u , n ,m,ZOA ,n ,m, AOA
(t ; ) Pn ,m
T
exp j
n ,m
n ,m 1 exp j n ,m
Frx,u , n ,m,ZOA ,n ,m, AOA
H
u , s ,n ,m
n ,m exp j n ,m
1
exp j n ,m
Ftx,s , n ,m,ZOD ,n ,m, AOD
exp
j 2 rˆrxT ,n ,m .d rx,u
j 2 rˆtxT,n ,m .d tx,s
exp
F
tx,s , n ,m,ZOD ,n ,m, AOD
(7.6-3)
f
f
rˆ T .v
exp j 2 rx,n ,m t n ,m
0
B B
with f is the wavelength on frequency f f c , f c , which can be implemented by user's own
2 2
method. The delay (TOA) for ray m in cluster n for a link between Rx antenna u and Tx antenna s is given by:
u ,s,n,m n,m 1c rˆrxT ,n,m .d rx,u 1c rˆtxT,n,m .dtx,s (7.6-4)
Note that Equation (7.6-3) only considers the delays n, m intentionally. If unequal ray powers are considered, Pn , m are
generated according to Clause 7.6.2.2. Otherwise, ray powers are equal within a cluster, i.e., Pn , m Pn M for all m.
Note: this model is developed assuming plane wave propagation.
7.6.2.2 Modelling of intra-cluster angular and delay spreads
With large antenna arrays or large bandwidths, the angle and/or delay resolution can be larger than what the fast fading
model in Clause 7.5 is designed to support. To model this effect, the following modifications to Step 7 in Clause 7.5 can
be optionally used.
1. The offset angles m in (7.5-13), (7.5-18) and (7.5-20) are generated independently per cluster and ray using:
n, m,AOA,AOD,ZOA, ZOD ~ unif 2,2 (7.6-5)
where unif a, b denotes the continuous uniform distribution in the interval a, b . These random variables may
further be modelled as spatially consistent with correlation distance equal to the cluster-specific random variable
correlation distance of Table 7.6.3.1-2.
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2. The relative delay of m-th ray is given by
n ,m n,m min n,i iM1 , where n is the cluster index,
n,m ~ unif 0, 2cDS , the cluster DS cDS is given in Table 7.5-6. These random variables may further be
modelled as spatially consistent with correlation distance equal to the cluster-specific random variable
correlation distance of Table 7.6.3.1-2. In this case, the sub-cluster mapping according to (7.5-26) and Table 7.5-
5 shall not be applied. The delays to be used in Equation (7.6-3) are given by n ,m n n ,m .
3. Ray powers are determined unequally by the following process:
Pn,m
The power of m-th ray in n-th cluster is given by Pn ,m Pn M
for m = 1,…,M, where
P
m 1
n ,m
2 n ,m,AOA 2 n ,m,AOD
Pn,m exp n ,m exp exp
cDS cASA cASD
(7.6-6)
2 n ,m,ZOA 2 n ,m,ZOD
exp exp
cZSA cZSD
and cDS , cASA , cASD , and cZSA are respectively the intra-cluster delay spread and the corresponding intra-cluster
angular spread that are given in Table 7.5-6. The cluster zenith spread of departure is given by
3
cZSD 10 lgZSD , (7.6-7)
8
with lgZSD being defined in Tables 7.5-7, 7.5-8, 7.5-9, 7.5-10 and 7.5-11.
4. The number of rays per cluster shall be calculated as follows:
M min max M t M AODM ZOD ,20, M max (7.6-8)
where:
- M t 4kcDSB
Dh
M AOD 4kcASD
180
-
Dv
M ZOD 4kcZSD
180
-
- M max is the upper limit of M , and it should be selected by the user of channel model based on the trade-off
between simulation complexity and accuracy.
- Dh and Dv are the array size in m in horizontal and vertical dimension, B is bandwidth in Hz, c ASD and c ZSD
are the cluster spreads in degrees, and is the wavelength.
- k is a "sparseness" parameter with value 0.5.
It is noted that each MPC may have different AOD, ZOD, and delay.
7.6.3 Spatial consistency
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7.6.3.1 Spatial consistency procedure
A new procedure, namely a spatial consistency procedure, can be used for both cluster-specific and ray-specific
random variables to be generated in various channel generation steps in Clause 7.5, so that they are spatially consistent
for drop-based simulations. Alternatively, this can be used together with Procedure B described in Clause 7.6.3.2 for
spatially consistent mobility simulations.
The procedure can be considered as a 2D random process (in the horizontal plane) given the UT locations based on the
parameter-specific correlation distance values for spatial consistency, specified in Table 7.6.3.1-2. The cluster specific
random variables include:
- Cluster specific random delay in Step 5;
- Cluster specific shadowing in Step 6; and
- Cluster specific offset for AOD/AOA/ZOD/ZOA in Step 7.
- Cluster specific sign for AOD/AOA/ZOD/ZOA in Step 7.
- Optionally in case of large bandwidth as described in Clause 7.6.2.2 the procedure may apply as well for the
parameters of rays within a cluster.
The procedure shall apply to each cluster before sorting the delay. Cluster specific sign for AOD/AOA/ZOD/ZOA in
Step 7 shall be kept unchanged per simulation drop even if UT position changes during simulation. The ray specific
random variables include:
- Random coupling of rays in Step 8;
- XPR in Step 9; and
- Random phase in Step 10.
The random coupling of rays in Step 8 and the intra-cluster delays in Step 11 shall be kept unchanged per simulation
drop even if UT position changes during simulation.
Table 7.6.3.1-2 Correlation distance for spatial consistency
Correlation RMa UMi UMa Indoor InF
distance in [m]
LOS NLOS O2I LOS NLOS O2I LOS NLOS O2I
Cluster and ray 50 60 15 12 15 15 40 50 15 10 10
specific random
variables
LOS/NLOS state 60 50 50 10 𝑑𝑐𝑙𝑢𝑡𝑡𝑒𝑟 ⁄2
Indoor/outdoor 50 50 50 N/A N/A
state
7.6.3.2 Spatially-consistent UT/BS mobility modelling
For mobility simulation enhancement, two alternative spatial consistency procedures – Procedure A and Procedure B –
are described as follows. The procedures presented below consider the downlink direction same as in Clause 7.5.
Procedure A:
For t0 0 when a UT/BS is dropped into the network, spatially consistent powers/delays/angles of clusters are
generated according to Clause 7.6.3.1.
The updated distance of UT/BS should be limited within 1 meter, i.e. when v t 1 m , and the updated procedure in
t
the following should take the closest realization instead of 0 0 . In case the absolute time of arrival modeling of
clause 7.6.9 is simultaneously used, the update distance should be the minimum of 1 meter and 𝑑3D ⁄10.
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Attk tk 1 t , update channel cluster power/delay/angles based on UT/BS channel cluster power/delay/angles,
moving speed moving direction and UT position at tk 1 .
Cluster delay is updated as:
𝑟̂rx,𝑛 (𝑡𝑘−1 )𝑇 𝑣̅rx (𝑡𝑘−1 )+𝑟̂tx,𝑛(𝑡𝑘−1 )𝑇 𝑣̅tx (𝑡𝑘−1 )
𝜏̃𝑛 (𝑡𝑘−1 ) − ∆𝑡 for 𝑘 > 0
𝑐
𝜏̃𝑛 (𝑡𝑘 ) = { 𝑑3D (𝑡0 )
(7.6-9)
𝜏𝑛 (𝑡0 ) + 𝜏∆ (𝑡0 ) + for 𝑘 = 0
𝑐
where
c is the speed of light in [m/s];
𝑣̅tx (𝑡𝑘 ) and 𝑣̅rx (𝑡𝑘 ) are the BS and UTvelocity vectors respectively, with ‖𝑣𝑡𝑥 (𝑡𝑘 )‖2 = 𝑣𝑡𝑥 and ‖𝑣𝑟𝑥 (𝑡𝑘 )‖2 = 𝑣𝑟𝑥 ;
d3D t0 is the 3D distance between Rx antenna and Tx antenna at t 0 in [m];
n t0 are the cluster delays in [s] as in Step 11 in Clause 7.5;
0 for LOS
t0 , where n are the delays from Equation (7.5-1). In case the absolute time
min n n 1
N
for NLOS
0 for LOS
of arrival modelling of clause 7.6.9 is simultaneously used, t0 where Δ𝜏 is
for NLOS
determined according to clause 7.6.9.
Also,
sin n ,ZOA (tk 1 ) cos n ,AOA (tk 1 )
rˆrx ,n tk 1 sin n ,ZOA (tk 1 ) sin n ,AOA (tk 1 ) (7.6-10)
cos n ,ZOA (tk 1 )
where n,ZOA and n, AOA are cluster specific zenith and azimuth angles of arrival.
sin (𝜃𝑛,ZOD (𝑡𝑘−1 )) cos (𝜙𝑛,AOD (𝑡𝑘−1 ))
𝑟̂tx,𝑛 (𝑡𝑘−1 ) = sin (𝜃𝑛,ZOD (𝑡𝑘−1 )) sin (𝜙𝑛,AOD (𝑡𝑘−1 )) (7.6-10aa)
[ cos (𝜃𝑛,ZOD (𝑡𝑘−1 )) ]
where 𝜃𝑛,ZOD and 𝜙𝑛,AOD are the cluster specific zenith and azimuth angles of departure.
After updating the delays according to equation (7.6-9), the delays over the mobility range are normalized. Equation
(7.5-2) of the fast fading model is replaced by
n tk ~n tk min ~n tk nN1 (7.6-10a)
in which t k covers the entire duration of the mobility model.
Cluster powers are updated as in Step 6 using the cluster delays from Equation (7.6-10a).
Cluster departure angles ( n , ZOD and n , AOD ) and arrival angles ( n , ZOA and n , AOA ) are updated using a cluster-wise
transformation of the UT/BS velocity vector given by
′ 𝑣̅rx (𝑡𝑘 )−𝑣̅tx (𝑡𝑘 ) for LOS
𝑣̅𝑛,rx (𝑡𝑘 ) = { (7.6-10b)
𝑅𝑛,rx ∙ 𝑣̅rx (𝑡𝑘 ) − 𝑣̅tx (𝑡𝑘 ) for NLOS
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′ 𝑣̅tx (𝑡𝑘 )−𝑣̅rx (𝑡𝑘 ) for LOS
𝑣̅𝑛,tx (𝑡𝑘 ) = { (7.6-10c)
𝑅𝑛,tx ∙ 𝑣̅tx (𝑡𝑘 ) − 𝑣̅rx (𝑡𝑘 ) for NLOS
with
1 0 0
𝜋 𝜋
𝑅𝑛,rx = 𝑅𝑍 (𝜙𝑛,AOD (𝑡𝑘 ) + 𝜋) ∙ 𝑅𝑌 ( −𝜃𝑛,ZOD (𝑡𝑘 )) ∙ [0 𝑋𝑛 0] ∙ 𝑅𝑌 ( 2 − 𝜃𝑛,ZOA (𝑡𝑘 )) ∙ 𝑅𝑍 (−𝜙𝑛,AOA (𝑡𝑘 )),
2
0 0 1
and
1 0 0
𝜋 𝜋
𝑅𝑛,tx = 𝑅𝑍 (−𝜙𝑛,AOD (𝑡𝑘 )) ∙ 𝑅𝑌 ( −𝜃𝑛,ZOD (𝑡𝑘 )) ∙ [0 𝑋𝑛 0] ∙ 𝑅𝑌 ( 2 −𝜃𝑛,ZOA (𝑡𝑘 )) ∙ 𝑅𝑍 (𝜙𝑛,AOA (𝑡𝑘 ) + 𝜋),
2
0 0 1
where RY and RZ being the rotation matrices around y-axis and z-axis as defined in Equation (7.1-2). Random
variable X n 1,1 is sampled from a uniform distribution on a NLOS cluster basis and is not changed during the
UT mobility within a drop. The cluster specific decorrelation distances are 60m, 15m, 50m and 10m for RMa, UMi,
UMa and Indoor scenarios, respectively.
Now, the departure and arrival angles in radians are updated as:
′
𝑣̅𝑛,rx (𝑡𝑘−1 )𝑇 𝜙
̂ (𝜃𝑛,ZOD (𝑡𝑘−1 ),𝜙𝑛,AOD (𝑡𝑘−1 ))
𝜙𝑛,AOD (𝑡𝑘 ) = 𝜙𝑛,AOD (𝑡𝑘−1 ) + ∆𝑡 (7.6-11)
𝑐∙𝜏̃𝑛 (𝑡𝑘−1 ) sin(𝜃𝑛,𝑍𝑂𝐷 (𝑡𝑘−1 ))
′
𝑣̅𝑛,rx (𝑡𝑘−1 )𝑇 𝜃
̂ (𝜃𝑛,ZOD (𝑡𝑘−1 ),𝜙𝑛,AOD (𝑡𝑘−1 ))
𝜃𝑛,ZOD (𝑡𝑘 ) = 𝜃𝑛,ZOD (𝑡𝑘−1 ) + ∆𝑡 (7.6-12)
𝑐∙𝜏̃𝑛(𝑡𝑘−1 )
and
′ (𝑡 𝑇̂
𝑣̅𝑛,tx 𝑘−1 ) 𝜙 (𝜃𝑛,ZOA (𝑡𝑘−1 ),𝜙𝑛,AOA (𝑡𝑘−1 ))
𝜙𝑛,AOA (𝑡𝑘 ) = 𝜙𝑛,AOA (𝑡𝑘−1 ) + ∆𝑡 (7.6-13)
𝑐∙𝜏̃𝑛 (𝑡𝑘−1 ) sin(𝜃𝑛,ZOA (𝑡𝑘−1 ))
′ (𝑡 𝑇̂
𝑣̅𝑛,tx 𝑘−1 ) 𝜃 (𝜃𝑛,ZOA (𝑡𝑘−1 ),𝜙𝑛,AOA (𝑡𝑘−1 ))
𝜃𝑛,ZOA (𝑡𝑘 ) = 𝜃𝑛,ZOA (𝑡𝑘−1 ) + ∆𝑡, (7.6-14)
𝑐∙𝜏̃𝑛(𝑡𝑘−1 )
with ˆ , and ˆ , being the spherical unit vectors defined in Equations (7.1-13) and (7.1-14).
Procedure B:
In procedure B, spatial or time evolution of the channel is obtained by generating channel realizations separately for all
links to different Rx positions using Steps 1-12 of Clause 7.5 together with the spatially consistent procedure of Clause
7.6.3.1. In the case of mobility these positions may be a function of time along one or more Rx trajectories.
Furthermore, to ensure that the spatial or time evolution of delays and angles are within reasonable limits, Steps 5, 6,
and 7 in Clause 7.5 should be replaced by the below procedure.
Note: For implementation purposes, LSPs and SSPs may be interpolated within the coherence length or time of the
respective parameter.
Step 5: Generate cluster delays n , with n 1, N .
N delays are drawn randomly from a uniform distribution.
n ~ unif 0, 2 10
lgDS lgDS
(7.6-15)
Normalise the delays by subtracting the minimum delay: n n min n . The autocorrelation distance for n is
lgDS lgDS
2c 10 .
In the case of LOS, set the delay of the first cluster 1 to 0.
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Step 6: Generate N arrival angles and departure angles for both azimuth and elevation using (7.6-16) instead of (7.5-9)
and (7.5-14).
unif 1,1
lgASA lgASA
n, AOA ~ 2 10 (7.6-16)
with n 1, N . This step is repeated independently for AOD, AOA, ZOD, and ZOA with corresponding maximum
angles for the uniform distribution. In case of LOS, set the angles of the first cluster ( 1, AOA , etc) to 0.
lgDS lgDS
The autocorrelation distances are 2c 10 for AOD, and ZOD, while they are fixed to 50m for AOA, and
ZOA.
Offset angles etc are applied in the modified Step 7b below after cluster powers have been calculated.
Step 7: Generate cluster powers Pn
Cluster powers are calculated assuming a single slope exponential power profile and Laplacian angular power profiles.
The cluster powers are determined by
2 n,AOA 2 n,AOD
Pn exp n exp exp
DS ASA ASD
(7.6-17)
2 n,ZOA 2 n,ZOD Zn
exp exp 10 10
ZSA ZSD
where Z n ~ N (0, 2 ) (autocorrelation distance same as for shadow fading) is the per cluster shadowing term in [dB].
Delay spread DS and angular spreads ASA , ASD , ZSA , ZSD are generated in Step 4 of Clause 7.5. Normalize
the cluster powers so that the sum of all cluster powers is equal to one, i.e.,
Pn
Pn (7.6-17a)
P
N
n1 n
In the case of LOS condition, substitute DS with 1 K R 2 DS and { ASA , ASD , ZSA , ZSD } with {
1 K R ASA , 1 K R ASD , 1 K R ZSA , 1 K R ZSD } respectively to preserve the delay and
angular spreads. K R is the Ricean K-factor as generated in Step 4 converted to linear scale. Furthermore, an additional
specular component is added to the first cluster. Power of the single LOS ray is:
KR
P1, LOS
KR 1 (7.6-17b)
and the cluster powers are not normalized as in equation (7.6-17a), but:
Pn
n 1P1, LOS
1
Pn
K R 1 Pn
N
n 1 (7.6-17c)
where (.) is Dirac's delta function.
Assign the power of each ray within a cluster as Pn / M, where M is the number of rays per cluster.
Step 7b: Apply offset angles
The ray AOA angles are determined by
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n,m,AOA n,AOA LOS ,AOA c ASA m (7.6-17d)
where LOS,AOA is the LOS direction defined in the network layout description, see Step1c.
The generation of AOD ( n , m , AOD ) follows a procedure similar to AOA as described above.
The ZOA angles are determined by
n,m,ZOA n,ZOA ZOA cZSA m (7.6-17e)
where ZOA 900 if the BS-UT link is O2I and ZOA LOS ,ZOA otherwise. The LOS direction is defined in the
network layout description, see Step1c.
The ZOA angles are determined by
lgZSD
n,m,ZOD n,ZOD LOS ,ZOD offset,ZOD (3 8)(10 ) m (7.6-17f)
where lgZSD is the mean of the ZSD log-normal distribution.
Some of the delay and angle spreads and standard deviations used in equations (7.6-15) and (7.6-16) may be frequency-
dependent. In the case of multi-frequency simulations according to Clause 7.6.5, the largest value among all the
simulated frequencies should be used in (7.6-15) and (7.6-16) so that the cluster and ray delays and angles (but not the
powers or the resulting delay or angular spreads) are the same for all frequencies.
7.6.3.3 LOS/NLOS, indoor states and O2I parameters
The LOS state can be determined according to the spatial consistency procedure for random variables as mentioned in
Clause 7.6.3.1, by comparing a realization of a random variable generated with distance-dependent LOS probability. If
the realization is less than the LOS probability, the state is LOS; otherwise NLOS. Decision of LOS and NLOS status
should be used in Step 2 in Clause 7.5 if this advanced simulation is performed.
The same procedure can be applied for determining the indoor state, with the indoor probability instead of the LOS
probability.
The correlation distance for LOS state and indoor/outdoor is specified in Table 7.6.3.1-2.
The indoor distance can be modeled as the minimum of two spatially consistent uniform random variables within (0,
25) meters with correlation distance 25m.
Note in case the UT is in an indoor state, the pathloss model changes and a penetration loss is considered. For details on
the model, see Clause 7.4.3. Here, the focus is on modelling aspects with respect to spatial consistency. As described in
Clause 7.4.3, the penetration loss deviation σp represents variations within and between buildings of the same type. For
spatial consistency this can be modeled as a spatially consistent random variable with correlation distance 10m, see
Clause 7.6.3.1. The "building type" is determined using a spatially consistent uniform random variable with correlation
distance 50 m. The building type is determined by comparing the random variable with P1, where P1 is the probability of
the building type with low loss penetration. If the realization of the random variable is less than P 1, the building type is
low loss; otherwise the building type is high loss.
The cluster-specific and ray-specific random variables as defined in Clause 7.6.3.1 on the same floor are generated in
the spatial consistency modelling; otherwise, these variables across different floors are uncorrelated.
In case there is a transition from LOS to NLOS due to UT mobility, there will be a hard transition in the channel
response. This is because pathloss and LS parameters are different for these states, leading automatically to very
different channel realizations. To circumvent such hard transitions the optional soft LOS state can be considered to
determine the PL and the channel impulse responses containing characteristics of both LOS and NLOS. Soft LOS state
LOSsoft is generated by floating numbers between 0 (NLOS) and 1 (LOS) in the spatial consistency modelling. The
value of LOSsoft is determined by
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20
LOSsoft
1 1
arctan G F d , (7.6-18)
2
where:
- G is a spatially consistent Gaussian random variable with correlation distance according to Table 7.6.3.1-2;
- F d 2erf -1 2 PrLOS d 1 ; and
- PrLOS d is the distance dependent LOS probability function.
After LOSsoft is obtained, Steps 2-12 of the channel coefficient generation described in Clause 7.5 are performed
twice, once with the propagation condition in Step 2 set as LOS and once with the propagation condition in Step 2 set as
H LOS and H NLOS respectively, where H LOS is generated
NLOS. The resulting channel coefficients are denoted as
NLOS
with the LOS path loss formula and channel model parameters while is H generated using the NLOS path loss
formula and channel model parameters. The channel matrix H with soft LOS state is determined from a linear
LOS NLOS
combination of H and H as:
H LOSsoft H LOSLOSsoft H NLOS 1 LOSsoft
2
(7.6-19)
It is noted that soft indoor/outdoor states are not modelled in this TR. Thus the model does not support transitions
between indoor/outdoor states in mobility simulations.
7.6.3.4 Applicability of spatial consistency
According to the characteristics of different cluster specific parameters and ray specific parameters, the following types
on spatial consistency are defined:
- Site-specific: parameters for different BS-UT links are uncorrelated, but the parameters for links from co-sited
sectors to a UT are correlated.
- All-correlated: BS-UT links are correlated.
In Table 7.6.3.4-1, correlation type for each large scale parameter, cluster specific parameter and ray specific parameter
is clarified.
Table 7.6.3.4-1: Correlation type among TRPs
Parameters Correlation type
Delays Site-specific
Cluster powers Site-specific
AOA/ZOA/AOD/ZOD offset Site-specific
AOA/ZOA/AOD/ZOD sign Site-specific
Random coupling Site-specific
XPR Site-specific
Initial random phase Site-specific
LOS/NLOS states Site-specific
Blockage (Model A) All-correlated
O2I penetration loss All-correlated
Indoor distance All-correlated
Indoor states All-correlated
Spatial consistency is not modelled in the following situations:
- Different link types, e.g., outdoor LOS, outdoor NLOS or O2I
- UE locates on different floors
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Table 7.6.3.4-2, lists conditions in which each large scale parameter, cluster specific parameter and ray specific parameter
is assumed not spatially correlated.
Table 7.6.3.4-2: Conditions when parameters are not spatially correlated.
Parameters Uncorrelated states
Delays Outdoor LOS/outdoor NLOS/O2I (different floors)
Cluster powers Outdoor LOS/outdoor NLOS/O2I (different floors)
AOA/ZOA/AOD/ZOD offset Outdoor LOS/outdoor NLOS/O2I (different floors)
AOA/ZOA/AOD/ZOD sign Outdoor LOS/outdoor NLOS/O2I (different floors)
Random coupling Outdoor LOS/outdoor NLOS/O2I (different floors)
XPR Outdoor LOS/outdoor NLOS/O2I (different floors)
Initial random phase Outdoor LOS/outdoor NLOS/O2I (different floors)
Blockage Outdoor/O2I (different floors)
Standard deviation for O2I penetration loss Different building types, i.e., high/low loss
7.6.4 Blockage
Blockage modelling is an add-on feature to the channel model. The method described in the following applies only
when this feature is turned on. In addition, the temporal variability of the blockage modelling parameters is on-demand
basis. It is also noted that the modelling of the blockage does not change LOS/NLOS state of each link.
When blockage model is applied, the channel generation in Clause 7.5 should have several additional steps between
Step 9 and 10 as illustrated in Figure 7.6.4-1.
Figure.7.6.4-1 Channel generation procedure with blockage model
Two alternative models (Model A and Model B) are provided for the blockage modelling. Both approaches have their
own use cases. Model A is applicable when a generic and computationally efficient blockage modelling is desired. Model
B is applicable when a specific and more realistic blocking modelling is desired.
7.6.4.1 Blockage model A
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Model A adopts a stochastic method for capturing human and vehicular blocking.
Step a: Determine the number of blockers.
Multiple 2-dimensional (2D) angular blocking regions, in terms of centre angle, azimuth and elevation angular span are
generated around the UT. There is one self-blocking region, and K = 4 non-self-blocking regions, where K may be
changed for certain scenarios (e.g., higher blocker density). Note that the self-blocking component of the model is
important in capturing the effects of human body blocking.
Step b: Generate the size and location of each blocker.
For self-blocking, the blocking region in UT LCS is defined in terms of elevation and azimuth angles, ( 'sb , 'sb ) and
azimuth and elevation angular span ( xsb , ysb ).
x
' , ' 'sb
ysb y x
' 'sb sb , 'sb sb ' 'sb sb (7.6-20)
2 2 2 2
where the parameters are described in Table 7.6.4.1-1.
Table 7.6.4.1-1: Self-blocking region parameters.
sb xsb 'sb ysb
Portrait mode 260o 120o 100o 80o
Landscape mode 40o 160o 110o 75o
For non-self-blocking k = 1, …, 4, the blocking region in GCS is defined by
, k k , k k
yk yk xk xk
(7.6-21)
2 2 2 2
where the parameters are described in Table 7.6.4.1-2, as well as the distance r between the UT and the blocker.
Table 7.6.4.1-2: Blocking region parameters.
Blocker index (k = 1, …, 4) k xk k yk r
InH scenario Uniform in Uniform in 90o Uniform in 2m
[0o, 360o] [15o, 45o] [5o, 15o]
UMi, UMa, RMa scenarios Uniform in Uniform in 90o 5o 10 m
[0o, 360o] [5o, 15o]
Step c: Determine the attenuation of each cluster due to blockers.
The attenuation of each cluster due to self-blocking corresponding to the centre angle pair ( 'sb , 'sb ), is 30 dB provided
xsb y
that sb
AOA sb sb . Otherwise, the attenuation is 0 dB.
and ZOA
2 2
The attenuation of each cluster due to the non-self-blocking regions (k=1, …, 4) is given by
LdB 20 log 10 1 FA1 FA2 FZ1 FZ 2 (7.6-22)
provided that AOA k xk and ZOA k yk . Otherwise, the attenuation is 0 dB. The terms in the above
equation are given as
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tan 1 1
1
r
2 cos A1 | A2 | Z1 | Z 2
FA1 | A2 | Z1 | Z 2 (7.6-23)
where
x
A1 AOA k k , (7.6-24)
2
x
A2 AOA k k , (7.6-25)
2
y
Z1 ZOA k k , (7.6-26)
2
y
Z 2 ZOA k k . (7.6-27)
2
In the above formula for FA1| A2 |Z1|Z 2 , is the wavelength. The appropriate signs ( )within the tan 1 term are
described in Table 7.6.4.1-3.
Table 7.6.4.1-3: Description of signs
yk yk y yk
y k ZOA k ZOA k k ZOA k y k
2 2 2 2
xk (-, +) for A1 , A2 (-, +) for A1 , A2 (-, +) for A1 , A2
AOA k xk (+, -) for Z1 , Z 2 (+, +) for Z1 , Z 2 (-, +) for Z1 , Z 2
2
x x (+, +) for A1 , A2 (+, +) for A1 , A2 (+, +) for A1 , A2
k AOA k k (+, -) for Z1 , Z 2 (+, +) for Z1 , Z 2 (-, +) for Z1 , Z 2
2 2
x (+, -) for A1 , A2 (+, -) for A1 , A2 (+, -) for A1 , A2
xk AOA k k
2 (+, -) for Z1 , Z 2 (+, +) for Z1 , Z 2 (-, +) for Z1 , Z 2
Step d: Spatial and temporal consistency of each blocker.
The centre of the blocker is generated based on a uniformly distributed random variable, which is temporally and spatially
consistent. The two-dimensional autocorrelation function R x , t can be described with sufficient accuracy by the
exponential function
R x , t exp x t (7.6-28)
d corr tcorr
The spatial correlation distance dcorr for the random variable determining the centre of the blocker is given in Table
7.6.4.1-4 for different scenarios.
Table 7.6.4.1-4: Spatial correlation distance for different scenarios.
UMi UMa RMa InH
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Spatial correlation LOS NLOS O2I LOS NLOS O2I LOS NLOS O2I LOS NLOS
distance dcorr in [m]
for the random
variable determining 10 10 5 10 10 5 10 10 5 5 5
the centre of the
blocker
The correlation time is defined by tcorr dcorr v , where v is the speed of the moving blocker.
Note: The rectangular blocker description is chosen for self-blocking region with the specific choices of ( 'sb , 'sb )
assumed here. Generalization of this description to other choices should be done with care as the rectangular description
may not be accurate.
7.6.4.2 Blockage model B
Model B adopts a geometric method for capturing e.g., human and vehicular blocking.
Step a: Determine blockers
A number, K , of blockers are modelled as rectangular screens that are physically placed on the map. Each screen has
the dimension by height ( hk ) and width ( wk ), with the screen centre at coordinate xk , yk , zk .
Note:
- The number of blockers ( K ), their vertical and horizontal extensions ( hk and wk ), locations xk , yk , zk ,
density, and movement pattern (if non-stationary) are all simulation assumptions, to allow different blocking
scenarios to be constructed depending on the need of the particular simulation study.
Recommended parameters for typical blockers are provided in Table 7.6.4.2-5.
- The blocking effect diminishes with increasing distance to the blocker. For implementation purposes it may be
sufficient to consider only the K nearest blockers or the blockers closer than some distance from a specific UT.
Table 7.6.4.2-5: Recommended blocker parameters
Typical set of blockers Blocker dimensions Mobility pattern
Indoor; Outdoor; InF Human Cartesian: w=0.3m; h=1.7m Stationary or up to 3 km/h
Outdoor Vehicle Cartesian: w=4.8m; h=1.4m Stationary or up to 100 km/h
InF AGV Cartesian: w=3m; h=1.5m Up to 30 km/h
InF Industrial robot Cartesian: w=2m; h=0.2m Up to 3 m/s
Step b: Determine the blockage attenuation per sub-path
Attenuation caused by each blocker to each sub-path is modelled using a simple knife edge diffraction model and is given
by
LdB 20 log 10 1 Fh1 Fh2 Fw1 Fw2 (7.6-29)
where Fh1 , Fh2 and Fw1 , Fw2 account for knife edge diffraction at the four edges, and are given by
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1
tan
D1h1|h2|w1|w2 D2 h1|h2|w1|w2 r
2 , for direct path in LOS
Fh1|h2|w1|w2 (7.6-30)
tan 1
D1h1|h2|w1|w2 r '
2 , for all other paths
where is the wave length. As shown in Figure 7.6.4.2-2, D1h1|h2 |w1|w2 are the projected (onto the side and top view
planes) distances between the receiver and four edges of the corresponding blocker, and D2 h1|h2 |w1|w2 are the projected
(onto the side and top view planes) distances between the transmitter and four edges of the corresponding blocker. The
side view plane is perpendicular to the horizontal ground plane. The top view is perpendicular to the side view. For each
cluster, the blocker screen is rotated around its centre such that the arrival direction of the corresponding path is always
perpendicular to the screen. It should be noted that different rotations are required for each individual sub-path.
Meanwhile, the base and top edges of the screens are always parallel to the horizontal plane. As the screen is perpendicular
to each sub-path, r is the distance between the transmitter and receiver for the direct path in LOS, and r ' is the distance
between the blocker screen and receiver, projected onto the incoming sub-path direction, for all the other (NLOS) paths.
In the equation of Fh1|h2 |w1|w2 , the plus and minus signs are determined in such a way that, as shown in Figure 7.6.4.2-2,
- if the sub-path (terminated at the receiver or transmitter) does not intersect the screen in side view, minus sign is
applied for the shortest path among D1h1 and D1h2 in the NLOS case ( D1h1 D2h1 and D1h2 D2h2 in the
LOS case) and plus sign is applied for the other edge.
- if the sub-path (terminated at the receiver or transmitter) does not intersect the screen in top view, minus sign is
applied for the shortest path among D1w1 and D1w2 in the NLOS case ( D1w1 D2w1 and D1w2 D2w2 for the
LOS case) and plus sign is applied for the other edge.
- if the sub-path intersects the screen plus signs are applied for both edges.
For the case of multiple screens the total loss is given by summing the losses of each contributing screen in dB units.
The model according to option B is consistent in time, frequency and space, and is more appropriate to be used for
simulations with arbitrarily designated blocker density.
. Top View
D1w1 Rx
.
Rx D2w1 .
Tx . r . D1w2
(xk, yk, zk) w
Tx
.
D2w2
Side View
D2h1
D2h1
h . Tx
r h D1h1
D1h1
. .
Tx
D2h2
D2h2
. D1h2
Rx
r D1h2
Rx
Figure 7.6.4.2-2(a): Illustration of the geometric relation among blocker, receiver and transmitter for
the LOS path
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. Top View
D1w1 Rx
.
Rx .
r'
. . D1w2
(xk, yk, zk) w
.
Side View
h .
h D1h1
D1h1
. .
r'
. D1h2 Rx
D1h2
r'
Rx
Figure 7.6.4.2-2(b): Illustration of the geometric relation between blocker and receiver for NLOS paths
7.6.5 Correlation modelling for multi-frequency simulations
This clause describes how to generate parameters to reflect correlation across different frequencies for a BS-UT link, for
simulations involved with multiple frequencies.
For those simulations, the steps in Clause 7.5 should be revised according to the following:
- The parameters generated in Step 1 are the same for all the frequencies, except for antenna patterns, array
geometries, system center frequency and bandwidth.
- Propagation conditions generated in Step 2 are the same for all the frequencies. It is noted that soft LOS states
may be different due to frequency dependent function.
- The parameters generated in Step 4 are the same for all the frequencies, except for possibly frequency-dependent
scaling of e.g. delay spread and angular spreads according to the LSP tables. I.e. let x be a random variable
drawn from a Gaussian distribution: x ~ N(0,1). Then the delay spread at frequency f is DS(f)= 10^(lgDS(f) +
lgDS(f)x), where the same value of x is used for all frequencies. A corresponding procedure applies to each of
the angular spreads.
- The cluster delays and angles resulting from Steps 5-7 are the same for all frequency bands
- Per-cluster shadowing n in Step 6 are independently generated for the frequency bands.
- Cluster powers in Step 6 may be frequency-dependent.
- Steps 8-11 are independently applied for the frequency bands.
In addition, when blockage is modeled according to Clause 7.6.4, the positions of blockers are the same across all the
frequencies.
Note: The requirements above may not be fully aligned with the behavior of the model according to Clause 7.5,
since cluster delays and angles will be frequency-dependent in scenarios where the DS or AS is
frequency-dependent. The procedure below may alternatively be used to ensure that cluster delays and
angles are frequency-independently generated.
7.6.5.1 Alternative channel generation method
The alternative method replaces Steps 5-7 in Clause 7.5 with the below Steps 5'-7'. The inputs to the alternative method
are the delay and angular spreads determined according to Step 4 at an anchor frequency, e.g. 2 GHz: DS 0, ASD0,
ASA0, ZSD0, ZSA0, the delay and angular spreads determined according to Step 4 at a frequency of interest: DS, ASD,
ASA, ZSD, ZSA, and the number of clusters N from Table 7.5-6.
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Step 5': Generate nominal delays n and angles AOD ,n , ZOD
,n , AOA ,n , ZOA
,n .
Generate N delays from a single-sided exponential distribution with zero mean and standard deviation of r DS 0 ,
according to n r DS0 ln X n with X n ~ unif 0, 1 .
Generate N AODs from a wrapped Gaussian distribution with zero mean and standard deviation of r ASD 0 , according
to ,n argexp j r ASD 0Yn with Yn ~ N 0, 1 .
AOD
Generate N AOAs from a wrapped Gaussian distribution with zero mean and standard deviation of r ASA 0 , according
to ,n arg exp j r ASA 0 Z n with Z n ~ N 0, 1 .
AOA
Generate N ZODs from a wrapped Laplacian distribution with zero mean and standard deviation of r ZSD 0 , according
to
,n arg exp j r ZSD 0 sgn Vn 0.5 ln 1 2 Vn 0.5
ZOD
2 with Vn ~ unif 0, 1 .
Generate N ZOAs from a wrapped Laplacian distribution with zero mean and standard deviation of r ZSA 0 , according
to
,n arg exp j r ZSA 0 sgn Wn 0.5 ln 1 2 Wn 0.5
ZOA
2 with Wn ~ unif 0, 1 .
r is a proportionality factor, r=1.5. The principal value of the arg function should be used, e.g. (-180,180).
In case of LOS, set 1 0 , ,1 0 , AOA
AOD ,1 0 , ZOD
,1 0 , and ZOA
,1 0 .
Step 6': Generate cluster powers Pn .
Generate cluster powers as
AOD
,n g ASD AOA
2
,n g ASA
2
Pn exp n g DS 2 ZOD ,n g ZSA 10 Qn 10
,n g ZSD 2 ZOA
2 2
(7.6.30a)
where
Qn ~ N 0, 2 is the per cluster shadowing term in [dB] and
max r DS 0 DS, 0
g DS (7.6.30b)
DS r DS 0
g ASD
max r ASD 0 ASD 2 , 0
2
(7.6.30c)
ASD r ASD 0
g ASA
max r ASA 0 ASA 2 , 0
2
(7.6.30d)
ASA r ASA 0
max r ZSD 0 ZSD, 0
g ZSD (7.6.30e)
ZSD r ZSD 0
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max r ZSA 0 ZSA, 0
g ZSA (7.6.30f)
ZSA r ZSA 0
Normalize the cluster powers so that the sum of all cluster powers is equal to one, i.e.,
Pn
Pn (7.6.30g)
n1 Pn
N
or, in the case of LOS, so that
1 Pn KR
Pn (n 1) (7.6.30h)
1 KR n1 Pn 1 KR
N
where K R is the K-factor converted to linear scale.
Step 7': Generate delays n and angles AOD,n , AOA,n , ZOD ,n , ZOA ,n .
n
, for NLOS
n KR (7.6.30i)
1 n , for LOS
2
n,AOA LOS,AOA cASA m , for NLOS
n,m,AOA (7.6.30j)
1 K R n,AOA LOS,AOA cASA m , for LOS
n,AOD LOS,AOD cASD m , for NLOS
n,m,AOD (7.6.30k)
1 K R n,AOD LOS,AOD cASD m , for LOS
n,ZOA LOS,ZOA cZSA m , for NLOS
n,m,ZOA (7.6.30l)
1 K R n,ZOA LOS,ZOA cZSA m , for LOS
n,ZOD LOS,ZOD cZSD m , for NLOS
n,m,ZOD (7.6.30m)
1 K R n,ZOD LOS,ZOD cZSD m , for LOS
Repeat Steps 6'-7' for each frequency of interest, reusing the delays and angles from Step 5' for all frequencies.
Note: The resulting delay and angular spreads of channels generated with this alternative method will be similar but not
identical to when using Steps 5-7 in Clause 7.5.
7.6.6 Time-varying Doppler shift
The Doppler shift generally depends on the time evolution of the channel as it is defined as the derivative of the
channels phase over time. It can be the result from Tx, Rx, or scatterer movement. The more general form of the
exponential Doppler term as used in Equation (7.5-22) is given by
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exp j 2
ˆrx,n,m (~
t T
r t )
~
v t d~t
. (7.6-31)
t0 0
Here, rˆrx, n, m (t ) is the normalized vector that points into the direction of the incoming wave as seen from the Rx at
time t . v t denotes the velocity vector of the Rx at time t , while t 0 denotes a reference point in time that defines
the initial phase, e.g. t 0 0.
Note that Equation (7.5-22) only holds for time-invariant Doppler shift, i.e. rˆrxT ,n,m (t ) v t rˆrxT,n,m v .
7.6.7 UT rotation
UT rotation modelling is an add-on feature. When modelled, Step 1 in Clause 7.5 shall consider UT rotational motion.
Step 1:
Add: h) Give rotational motion of UT in terms of its bearing angle, downtilt angle and slant angle.
7.6.8 Explicit ground reflection model
In case the ground reflection shall be modelled explicitly, Equation (7.5-30) has to be replaced by
, s ( , t )
H uLOS
1
H uNLOS LOS, t
KR 1
,s
(7.6-32)
K R LOS
H u , s ,1 (t ) LOS 3D H uGR
, s (t ) GR
d
KR 1 d GR
with the delays for the ground reflected and the LOS paths being defined by their lengths, according to the Tx-Rx
separation d 2D and the Tx and Rx heights htx and hrx respectively, as
d GR htx hrx 2 d 2D
2
GR , (7.6-33)
c c
and
d htx hrx 2 d 2D2
LOS 3D . (7.6-34)
c c
The channel coefficient for the ground reflected path is given by
Frx ,u , GR, ZOA ,GR, AOA R||GR 0 Ftx , s , GR, ZOD ,GR, AOD
T
GR
(t )
Frx , u , GR, ZOA ,GR, AOA 0 RGR Ftx, s , GR, ZOD ,GR, AOD
H u,s
(7.6-35)
d ˆ
r T
.d rˆ T
.d ˆ
r T
.v
exp j 2 GR exp j 2 rx, GR rx ,u exp j 2 tx, GR tx, s exp j 2 rx, GR t
0 0 0 0
with the normalized vectors pointing towards the ground reflection point from the Tx
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sin GR, ZOD cos GR, AOD
rˆtx, GR er GR, ZOD , GR, AOD sin GR, ZOD sin GR, AOD (7.6-36)
cos GR, ZOD
and from the Rx side
sin GR, ZOA cos GR, AOA
rˆrx, GR er GR, ZOA ,GR, AOA sin GR, ZOA sin GR, AOA . (7.6-37)
cos GR, ZOA
The angles for the ground reflected path are given by the geometry assuming a flat surface with its normal pointing into
z-direction. The angles at the Tx side can be determined by
d 2D
GR, ZOD 180 - atan (7.6-37a)
hrx htx
GR,AOD LOS,AOD (7.6-37b)
and at the Rx side by
GR, ZOA GR, ZOD (7.6-37c)
GR,AOA GR,AOD 180 . (7.6-37d)
The reflection coefficients for parallel and perpendicular polarization on the ground, cf. [21], are given by
GR GR
cos GR,ZOD sin 2 GR,ZOD
0
R||GR 0 , (7.6-38)
GR GR
cos GR,ZOD sin GR,ZOD
2
0 0
and
GR
cos GR,ZOD sin 2 GR,ZOD
0
RGR , (7.6-39)
GR
cos GR,ZOD sin GR,ZOD
2
0
with the complex relative permittivity of the ground material given by
GR
r j . (7.6-40)
0 2 f c 0
The electric constant 0 is given by 8.854187817... × 10−12 F·m−1.
For applicable frequency ranges, the real relative permittivity can be modelled by
b
fc
r a 9 , (7.6-41)
10
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while the conductivity in [S/m] may be modelled by
d
f
c c9 , (7.6-42)
10
with fc being the center frequency in Hz.
A selection of material properties from [19] is given below. Since the simplified models for conductivity and relative
permittivity are only applicable for frequencies between 1 and 10 GHz, Figure 7.6.8-1 presents curves up to 100 GHz at
least for very, medium dry and wet ground, cf. [20].
Table 7.6.8-1 Material properties [19]
Relative permittivity Conductivity
Material class Frequency range in [GHz]
a b c d
Concrete 5.31 0 0.0326 0.8095 1-100
Brick 3.75 0 0.038 0 1-10
Plasterboard 2.94 0 0.0116 0.7076 1-100
Wood 1.99 0 0.0047 1.0718 0.001-100
Floorboard 3.66 0 0.0044 1.3515 50-100
Metal 1 0 107 0 1-100
Very dry ground 3 0 0.00015 2.52 1-10
Medium dry ground 15 −0.1 0.035 1.63 1-10
Wet ground 30 −0.4 0.15 1.30 1-10
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Figure 7.6.8-1: Relative permittivity and conductivity as a function of frequency [20]
7.6.9 Absolute time of arrival
To support simulations in which absolute time of arrival is important, the propagation time delay due to the total path
length is considered in step 11 of the fast fading model as follows. The impulse response in NLOS is determined using
equation (7.6-43) instead of (7.5-27) and the impulse response in LOS is determined using equation (7.6-44) instead of
(7.5-30), where 𝑐 is the speed of light. Δ𝜏 is generated from a lognormal distribution with parameters according to Table
7.6.9-1. Δ𝜏 is generated independently for links between the same UT and different BS sites. The excess delay in
NLOS, , should further be upper bounded by 2𝐿/𝑐, where 𝐿 is the largest dimension of the factory hall, i.e. 𝐿 =
max(length, width, height).
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𝑁𝐿𝑂𝑆 (𝜏,
𝐻𝑢,𝑠 𝑡) = ∑2𝑛=1 ∑3𝑖=1 ∑𝑚∈𝑅𝑖 𝐻𝑢,𝑠,𝑛,𝑚
𝑁𝐿𝑂𝑆 (𝑡)𝛿(𝜏
− 𝜏𝑛,𝑖 − 𝑑3𝐷 ⁄𝑐 − Δ𝜏) + ∑𝑁 𝑁𝐿𝑂𝑆
𝑛=3 𝐻𝑢,𝑠,𝑛 (𝑡)𝛿(𝜏 − 𝜏𝑛 − 𝑑3𝐷 ⁄𝑐 − Δ𝜏)(7.6-
43)
𝐿𝑂𝑆 (𝜏, 1 𝑁𝐿𝑂𝑆 (𝜏, 𝐾𝑅 𝐿𝑂𝑆
𝐻𝑢,𝑠 𝑡) = √ 𝐻𝑢,𝑠 𝑡) + √ 𝐻𝑢,𝑠,1 (𝑡)𝛿(𝜏 − 𝜏1 − 𝑑3𝐷 ⁄𝑐 ). (7.6-44)
𝐾𝑅 +1 𝐾𝑅 +1
Table 7.6.9-1: Parameters for the absolute time of arrival model
Scenarios InF-SL, InF-DL InF-SH, InF-DH
𝜇𝑙𝑔Δ𝜏 -7.5 -7.5
𝑙𝑔Δ𝜏 = 𝑙𝑜𝑔10 (Δ𝜏⁄1𝑠)
𝜎𝑙𝑔Δ𝜏 0.4 0.4
Correlation distance in the horizontal plane 6 11
[m]
7.6.10 Dual mobility
To support simulations that involve dual Tx and Rx mobility or scatterer mobility, the Doppler frequency component in
the channel coefficient generation in step 11 in clause 7.5 should be updated as follows.
For the LOS path, the Doppler frequency is given by
𝒓̂𝑻 ̅𝒓𝒙 +𝒓̂𝑻
𝒓𝒙,𝒏,𝒎 ∙𝒗 ̅𝒕𝒙
𝒕𝒙,𝒏,𝒎 ∙𝒗
𝒗𝒏,𝒎 = (7.6-45)
𝝀𝟎
where 𝒓̂𝒓𝒙,𝒏,𝒎 and 𝒓̂𝒕𝒙,𝒏,𝒎 are defined in (7.5-23) and (7.5-24) respectively, and
vrx vrx sin v,rx cos v,rx sin v,rx sin v,rx cosv,rx
T
vtx vtx sin v,tx cos v,tx sin v,tx sin v,tx cosv,tx
T
For all other paths, the Doppler frequency component is given by:
𝒓̂𝑻 ̅𝒓𝒙 +𝒓̂𝑻
𝒓𝒙,𝒏,𝒎 ∙𝒗 ̅𝒕𝒙 +𝟐𝜶𝒏,𝒎 𝑫𝒏,𝒎
𝒕𝒙,𝒏,𝒎 ∙𝒗
𝒗𝒏,𝒎 = (7.6-46)
𝝀𝟎
where 𝐷𝑛,𝑚 is a random variable from −𝑣𝑠𝑐𝑎𝑡𝑡 to 𝑣𝑠𝑐𝑎𝑡𝑡 , 𝛼𝑛,𝑚 is a random variable of Bernoulli distribution with mean
p, and 𝑣𝑠𝑐𝑎𝑡𝑡 is the maximum speed of the clutter. Parameter p determines the proportion of mobile scatterers and can
thus be selected to appropriately model statistically larger number of mobile scatterers (higher p) or statistically smaller
number of mobile scatterers (e.g. in case of a completely static environment: p=0 results in all scatteres having zero
speed). A typical value of p is 0.2.
7.6.11 Sources of EM interference
When the simulation includes sources of EM interference in industrial scenarios, the channel(s) between the
interferer(s) and the BS or UT should be modeled using the appropriate sub-scenario(s), e.g. clutter-embedded or
elevated Tx or Rx and low or high clutter density. Characterization of EM interference sources is out of the scope of
this TR.
7.6.12 Embedded devices
To simulate devices that are embedded inside machinery or enclosures, an additional loss parameter Lembedded is added to
the path loss in step 12. The additional path loss parameter Lembedded to embedded devices is left as a simulation
assumption, where it may be formulated e.g. as a value or range, or derived using a methodology similar to the building
penetration loss in clause 7.4.3.1.
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7.7 Channel models for link-level evaluations
7.7.1 Clustered Delay Line (CDL) models
The CDL models are defined for the full frequency range from 0.5 GHz to 100 GHz with a maximum bandwidth of 2
GHz. CDL models can be implemented by e.g., coefficient generation Step 10 and Step 11 in Clause 7.5 or generating
TDL model using spatial filter from Clause 7.7.4. Three CDL models, namely CDL-A, CDL-B and CDL-C are
constructed to represent three different channel profiles for NLOS while CDL-D and CDL-E are constructed for LOS,
the parameters of which can be found respectively in Table 7.7.1-1, Table 7.7.1-2 , Table 7.7.1-3, Table 7.7.1-4 and
Table 7.7.1-5.
Each CDL model can be scaled in delay so that the model achieves a desired RMS delay spread, according to the
procedure described in Clause 7.7.3. Each CDL model can also be scaled in angles so that the model achieves desired
angle spreads, according to the procedure described in Clause 7.7.5.1.
For LOS channel models, the K-factor of CDL-D and CDL-E can be set to a desired value following the procedure
described in Clause 7.7.6.
For modelling effect of beamforming in a simplified way, a brick-wall window can be applied to a delay-scaled CDL
model. The power shall be normalized after applying the window. A TDL model for simplified evaluations can be
obtained from the CDL model, according to this method.
The following step by step procedure should be used to generate channel coefficients using the CDL models.
Step 1: Generate departure and arrival angles
Generate arrival angles of azimuth using the following equation
n,m,AOA n,AOA cASA m , (7.7-0a)
Where n,AOA is the cluster AOA and cASA is the cluster-wise rms azimuth spread of arrival angles (cluster ASA) in
Tables 7.7.1.1 – 7.7.1.5 below, and m denotes the ray offset angles within a cluster given by Table 7.5-3. If angular
scaling according to Clause 7.7.5.1 is used, this is applied to the ray angles n ,m ,AOA , The generation of AOD ( n ,m ,AOD
), ZSA ( n ,m , ZOA ), and ZSD ( n ,m , ZOD ) follows a procedure similar to AOA as described above.
Step 2: Coupling of rays within a cluster for both azimuth and elevation
Couple randomly AOD angles n ,m ,AOD to AOA angles n ,m ,AOA within a cluster n. Couple randomly ZOD angles
n ,m , ZOD with ZOA angles n ,m , ZOA using the same procedure. Couple randomly AOD angles n ,m ,AOD with ZOD
angles n ,m , ZOD within a cluster n.
Step 3: Generate the cross polarization power ratios
Generate the cross polarization power ratios (XPR) for each ray m of each cluster n as
n,m 10 X /10 , (7.7-0b)
where X is the per-cluster XPR in dB from Tables 7.7.1.1 – 7.7.1.5.
Step 4: Coefficient generation
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Follow the same procedure as in Steps 10 and 11 in Clause 7.5, with the exception that all clusters are treated as
"weaker cluster", i.e. no further sub-clusters in delay should be generated. Additional clusters representing delay spread
of the stronger clusters are already provided in Tables 7.7.1.1 – 7.7.1.5.
Table 7.7.1-1. CDL-A
Cluster # Normalized delay Power in [dB] AOD in [°] AOA in [°] ZOD in [°] ZOA in [°]
1 0.0000 -13.4 -178.1 51.3 50.2 125.4
2 0.3819 0 -4.2 -152.7 93.2 91.3
3 0.4025 -2.2 -4.2 -152.7 93.2 91.3
4 0.5868 -4 -4.2 -152.7 93.2 91.3
5 0.4610 -6 90.2 76.6 122 94
6 0.5375 -8.2 90.2 76.6 122 94
7 0.6708 -9.9 90.2 76.6 122 94
8 0.5750 -10.5 121.5 -1.8 150.2 47.1
9 0.7618 -7.5 -81.7 -41.9 55.2 56
10 1.5375 -15.9 158.4 94.2 26.4 30.1
11 1.8978 -6.6 -83 51.9 126.4 58.8
12 2.2242 -16.7 134.8 -115.9 171.6 26
13 2.1718 -12.4 -153 26.6 151.4 49.2
14 2.4942 -15.2 -172 76.6 157.2 143.1
15 2.5119 -10.8 -129.9 -7 47.2 117.4
16 3.0582 -11.3 -136 -23 40.4 122.7
17 4.0810 -12.7 165.4 -47.2 43.3 123.2
18 4.4579 -16.2 148.4 110.4 161.8 32.6
19 4.5695 -18.3 132.7 144.5 10.8 27.2
20 4.7966 -18.9 -118.6 155.3 16.7 15.2
21 5.0066 -16.6 -154.1 102 171.7 146
22 5.3043 -19.9 126.5 -151.8 22.7 150.7
23 9.6586 -29.7 -56.2 55.2 144.9 156.1
Per-Cluster Parameters
Parameter cASD in [°] cASA in [°] cZSD in [°] cZSA in [°] XPR in [dB]
Value 5 11 3 3 10
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Table 7.7.1-2. CDL-B
Cluster # Normalized delay Power in [dB] AOD in [°] AOA in [°] ZOD in [°] ZOA in [°]
1 0.0000 0 9.3 -173.3 105.8 78.9
2 0.1072 -2.2 9.3 -173.3 105.8 78.9
3 0.2155 -4 9.3 -173.3 105.8 78.9
4 0.2095 -3.2 -34.1 125.5 115.3 63.3
5 0.2870 -9.8 -65.4 -88.0 119.3 59.9
6 0.2986 -1.2 -11.4 155.1 103.2 67.5
7 0.3752 -3.4 -11.4 155.1 103.2 67.5
8 0.5055 -5.2 -11.4 155.1 103.2 67.5
9 0.3681 -7.6 -67.2 -89.8 118.2 82.6
10 0.3697 -3 52.5 132.1 102.0 66.3
11 0.5700 -8.9 -72 -83.6 100.4 61.6
12 0.5283 -9 74.3 95.3 98.3 58.0
13 1.1021 -4.8 -52.2 103.7 103.4 78.2
14 1.2756 -5.7 -50.5 -87.8 102.5 82.0
15 1.5474 -7.5 61.4 -92.5 101.4 62.4
16 1.7842 -1.9 30.6 -139.1 103.0 78.0
17 2.0169 -7.6 -72.5 -90.6 100.0 60.9
18 2.8294 -12.2 -90.6 58.6 115.2 82.9
19 3.0219 -9.8 -77.6 -79.0 100.5 60.8
20 3.6187 -11.4 -82.6 65.8 119.6 57.3
21 4.1067 -14.9 -103.6 52.7 118.7 59.9
22 4.2790 -9.2 75.6 88.7 117.8 60.1
23 4.7834 -11.3 -77.6 -60.4 115.7 62.3
Per-Cluster Parameters
Parameter cASD in [°] cASA in [°] cZSD in [°] cZSA in [°] XPR in [dB]
Value 10 22 3 7 8
Table 7.7.1-3. CDL-C
Cluster # Normalized delay Power in [dB] AOD in [°] AOA in [°] ZOD in [°] ZOA in [°]
1 0 -4.4 -46.6 -101 97.2 87.6
2 0.2099 -1.2 -22.8 120 98.6 72.1
3 0.2219 -3.5 -22.8 120 98.6 72.1
4 0.2329 -5.2 -22.8 120 98.6 72.1
5 0.2176 -2.5 -40.7 -127.5 100.6 70.1
6 0.6366 0 0.3 170.4 99.2 75.3
7 0.6448 -2.2 0.3 170.4 99.2 75.3
8 0.6560 -3.9 0.3 170.4 99.2 75.3
9 0.6584 -7.4 73.1 55.4 105.2 67.4
10 0.7935 -7.1 -64.5 66.5 95.3 63.8
11 0.8213 -10.7 80.2 -48.1 106.1 71.4
12 0.9336 -11.1 -97.1 46.9 93.5 60.5
13 1.2285 -5.1 -55.3 68.1 103.7 90.6
14 1.3083 -6.8 -64.3 -68.7 104.2 60.1
15 2.1704 -8.7 -78.5 81.5 93.0 61.0
16 2.7105 -13.2 102.7 30.7 104.2 100.7
17 4.2589 -13.9 99.2 -16.4 94.9 62.3
18 4.6003 -13.9 88.8 3.8 93.1 66.7
19 5.4902 -15.8 -101.9 -13.7 92.2 52.9
20 5.6077 -17.1 92.2 9.7 106.7 61.8
21 6.3065 -16 93.3 5.6 93.0 51.9
22 6.6374 -15.7 106.6 0.7 92.9 61.7
23 7.0427 -21.6 119.5 -21.9 105.2 58
24 8.6523 -22.8 -123.8 33.6 107.8 57
Per-Cluster Parameters
Parameter cASD in [°] cASA in [°] cZSD in [°] cZSA in [°] XPR in [dB]
Value 2 15 3 7 7
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Table 7.7.1-4. CDL-D.
Cluster # Cluster PAS Normalized Delay Power in [dB] AOD in [°] AOA in [°] ZOD in [°] ZOA in [°]
Specular(LOS path) 0 -0.2 0 -180 98.5 81.5
1
Laplacian 0 -13.5 0 -180 98.5 81.5
2 Laplacian 0.035 -18.8 89.2 89.2 85.5 86.9
3 Laplacian 0.612 -21 89.2 89.2 85.5 86.9
4 Laplacian 1.363 -22.8 89.2 89.2 85.5 86.9
5 Laplacian 1.405 -17.9 13 163 97.5 79.4
6 Laplacian 1.804 -20.1 13 163 97.5 79.4
7 Laplacian 2.596 -21.9 13 163 97.5 79.4
8 Laplacian 1.775 -22.9 34.6 -137 98.5 78.2
9 Laplacian 4.042 -27.8 -64.5 74.5 88.4 73.6
10 Laplacian 7.937 -23.6 -32.9 127.7 91.3 78.3
11 Laplacian 9.424 -24.8 52.6 -119.6 103.8 87
12 Laplacian 9.708 -30.0 -132.1 -9.1 80.3 70.6
13 Laplacian 12.525 -27.7 77.2 -83.8 86.5 72.9
Per-Cluster Parameters
Parameter cASD in [°] cASA in [°] cZSD in [°] cZSA in [°] XPR in [dB]
Value 5 8 3 3 11
Table 7.7.1-5. CDL-E.
Cluster # Cluster PAS Normalized Delay Power in [dB] AOD in [°] AOA in [°] ZOD in [°] ZOA in [°]
Specular (LOS path) 0.000 -0.03 0 -180 99.6 80.4
1
Laplacian 0.000 -22.03 0 -180 99.6 80.4
2 Laplacian 0.5133 -15.8 57.5 18.2 104.2 80.4
3 Laplacian 0.5440 -18.1 57.5 18.2 104.2 80.4
4 Laplacian 0.5630 -19.8 57.5 18.2 104.2 80.4
5 Laplacian 0.5440 -22.9 -20.1 101.8 99.4 80.8
6 Laplacian 0.7112 -22.4 16.2 112.9 100.8 86.3
7 Laplacian 1.9092 -18.6 9.3 -155.5 98.8 82.7
8 Laplacian 1.9293 -20.8 9.3 -155.5 98.8 82.7
9 Laplacian 1.9589 -22.6 9.3 -155.5 98.8 82.7
10 Laplacian 2.6426 -22.3 19 -143.3 100.8 82.9
11 Laplacian 3.7136 -25.6 32.7 -94.7 96.4 88
12 Laplacian 5.4524 -20.2 0.5 147 98.9 81
13 Laplacian 12.0034 -29.8 55.9 -36.2 95.6 88.6
14 Laplacian 20.6419 -29.2 57.6 -26 104.6 78.3
Per-Cluster Parameters
Parameter cASD in [°] cASA in [°] cZSD in [°] cZSA in [°] XPR in [dB]
Value 5 11 3 7 8
7.7.2 Tapped Delay Line (TDL) models
The TDL models for simplified evaluations, e.g., for non-MIMO evaluations, are defined for the full frequency range
from 0.5 GHz to 100 GHz with a maximum bandwidth of 2 GHz.
Three TDL models, namely TDL-A, TDL-B and TDL-C, are constructed to represent three different channel profiles
for NLOS while TDL-D and TDL-E are constructed for LOS, the parameters of which can be found respectively in
Table 7.7.2-1, Table 7.7.2-2 , Table 7.7.2-3, Table 7.7.2-4 and Table 7.7.2-5.
The Doppler spectrum for each tap is characterized by a classical (Jakes) spectrum shape and a maximum Doppler shift
fD where f D v 0 . Due to the presence of a LOS path, the first tap in TDL-D and TDL-E follows a Ricean fading
distribution. For those taps the Doppler spectrum additionally contains a peak at the Doppler shift fS = 0.7 fD with an
amplitude such that the resulting fading distribution has the specified K-factor.
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Each TDL model can be scaled in delay so that the model achieves a desired RMS delay spread, according to the
procedure described in Clause 7.7.3.
For LOS channel models, the K-factor of TDL-D and TDL-E can be set to a desired value following the procedure
described in Clause 7.7.6.
Table 7.7.2-1. TDL-A
Tap # Normalized delay Power in [dB] Fading distribution
1 0.0000 -13.4 Rayleigh
2 0.3819 0 Rayleigh
3 0.4025 -2.2 Rayleigh
4 0.5868 -4 Rayleigh
5 0.4610 -6 Rayleigh
6 0.5375 -8.2 Rayleigh
7 0.6708 -9.9 Rayleigh
8 0.5750 -10.5 Rayleigh
9 0.7618 -7.5 Rayleigh
10 1.5375 -15.9 Rayleigh
11 1.8978 -6.6 Rayleigh
12 2.2242 -16.7 Rayleigh
13 2.1718 -12.4 Rayleigh
14 2.4942 -15.2 Rayleigh
15 2.5119 -10.8 Rayleigh
16 3.0582 -11.3 Rayleigh
17 4.0810 -12.7 Rayleigh
18 4.4579 -16.2 Rayleigh
19 4.5695 -18.3 Rayleigh
20 4.7966 -18.9 Rayleigh
21 5.0066 -16.6 Rayleigh
22 5.3043 -19.9 Rayleigh
23 9.6586 -29.7 Rayleigh
Table 7.7.2-2. TDL-B
Tap # Normalized delay Power in [dB] Fading distribution
1 0.0000 0 Rayleigh
2 0.1072 -2.2 Rayleigh
3 0.2155 -4 Rayleigh
4 0.2095 -3.2 Rayleigh
5 0.2870 -9.8 Rayleigh
6 0.2986 -1.2 Rayleigh
7 0.3752 -3.4 Rayleigh
8 0.5055 -5.2 Rayleigh
9 0.3681 -7.6 Rayleigh
10 0.3697 -3 Rayleigh
11 0.5700 -8.9 Rayleigh
12 0.5283 -9 Rayleigh
13 1.1021 -4.8 Rayleigh
14 1.2756 -5.7 Rayleigh
15 1.5474 -7.5 Rayleigh
16 1.7842 -1.9 Rayleigh
17 2.0169 -7.6 Rayleigh
18 2.8294 -12.2 Rayleigh
19 3.0219 -9.8 Rayleigh
20 3.6187 -11.4 Rayleigh
21 4.1067 -14.9 Rayleigh
22 4.2790 -9.2 Rayleigh
23 4.7834 -11.3 Rayleigh
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Table 7.7.2-3. TDL-C
Tap # Normalized delays Power in [dB] Fading distribution
1 0 -4.4 Rayleigh
2 0.2099 -1.2 Rayleigh
3 0.2219 -3.5 Rayleigh
4 0.2329 -5.2 Rayleigh
5 0.2176 -2.5 Rayleigh
6 0.6366 0 Rayleigh
7 0.6448 -2.2 Rayleigh
8 0.6560 -3.9 Rayleigh
9 0.6584 -7.4 Rayleigh
10 0.7935 -7.1 Rayleigh
11 0.8213 -10.7 Rayleigh
12 0.9336 -11.1 Rayleigh
13 1.2285 -5.1 Rayleigh
14 1.3083 -6.8 Rayleigh
15 2.1704 -8.7 Rayleigh
16 2.7105 -13.2 Rayleigh
17 4.2589 -13.9 Rayleigh
18 4.6003 -13.9 Rayleigh
19 5.4902 -15.8 Rayleigh
20 5.6077 -17.1 Rayleigh
21 6.3065 -16 Rayleigh
22 6.6374 -15.7 Rayleigh
23 7.0427 -21.6 Rayleigh
24 8.6523 -22.8 Rayleigh
Table 7.7.2-4. TDL-D.
Tap # Normalized delay Power in [dB] Fading distribution
0 -0.2 LOS path
1
0 -13.5 Rayleigh
2 0.035 -18.8 Rayleigh
3 0.612 -21 Rayleigh
4 1.363 -22.8 Rayleigh
5 1.405 -17.9 Rayleigh
6 1.804 -20.1 Rayleigh
7 2.596 -21.9 Rayleigh
8 1.775 -22.9 Rayleigh
9 4.042 -27.8 Rayleigh
10 7.937 -23.6 Rayleigh
11 9.424 -24.8 Rayleigh
12 9.708 -30.0 Rayleigh
13 12.525 -27.7 Rayleigh
NOTE: The first tap follows a Ricean distribution with a K-factor of K1 = 13.3 dB and a mean power of 0dB.
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Table 7.7.2-5. TDL-E.
Tap # Normalized delay Power in [dB] Fading distribution
0 -0.03 LOS path
1
0 -22.03 Rayleigh
2 0.5133 -15.8 Rayleigh
3 0.5440 -18.1 Rayleigh
4 0.5630 -19.8 Rayleigh
5 0.5440 -22.9 Rayleigh
6 0.7112 -22.4 Rayleigh
7 1.9092 -18.6 Rayleigh
8 1.9293 -20.8 Rayleigh
9 1.9589 -22.6 Rayleigh
10 2.6426 -22.3 Rayleigh
11 3.7136 -25.6 Rayleigh
12 5.4524 -20.2 Rayleigh
13 12.0034 -29.8 Rayleigh
14 20.6519 -29.2 Rayleigh
NOTE: The first tap follows a Ricean distribution with a K-factor of K1 = 22 dB and a mean power of 0dB.
7.7.3 Scaling of delays
The RMS delay spread values of both CDL and TDL models are normalized and they can be scaled in delay so that a
desired RMS delay spread can be achieved. The scaled delays can be obtained according to the following equation:
n,scaled n,model DSdesired (7.7-1)
in which
n ,model is the normalized delay value of the nth cluster in a CDL or a TDL model
n ,scaled is the new delay value (in [ns]) of the nth cluster
DSdesired is the wanted delay spread (in [ns])
The example scaling parameters are selected according to Table 7.7.3-1 where the values have been chosen such that
the RMS delay spreads span the range observed in measurements corresponding to the typical 5G evaluation scenarios.
For information purposes, examples of such RMS delay spreads for the different scenarios are given in Table 7.7.3-2
where the "short-delay profile" corresponds to the median RMS delay spread for LOS scenarios, while "normal-delay
profile" and "long-delay profile" correspond to the median and the 90th percentile RMS delay spread for NLOS
scenarios according to the channel parameters in Table 7.5-6. It can therefore be understood that a particular RMS delay
spread in Table 7.7.3-1 may occur in any scenario; however certain values may be more likely in some scenarios than in
others.
The example parameters given in Table 7.7.3-1 does not preclude the use of other scaling values if this is found
appropriate, for instance if additional scenarios are introduced or if e.g. the effect of beamforming needs to be captured
in a TDL. Both of these examples can potentially result in an increased range of experienced RMS delay spreads.
Table 7.7.3-1. Example scaling parameters for CDL and TDL models.
Model DSdesired
Very short delay spread 10 ns
Short delay spread 30 ns
Nominal delay spread 100 ns
Long delay spread 300 ns
Very long delay spread 1000 ns
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Table 7.7.3-2. Scenario specific scaling factors - for information only
Frequency [GHz]
Proposed Scaling Factor DSdesired in [ns] 2 6 15 28 39 60 70
Short-delay profile 20 16 16 16 16 16 16
Indoor office Normal-delay profile 39 30 24 20 18 16 16
Long-delay profile 59 53 47 43 41 38 37
Short-delay profile 65 45 37 32 30 27 26
UMi Street-canyon Normal-delay profile 129 93 76 66 61 55 53
Long-delay profile 634 316 307 301 297 293 291
Short-delay profile 93 93 85 80 78 75 74
UMa Normal-delay profile 363 363 302 266 249 228 221
Long-delay profile 1148 1148 955 841 786 720 698
Short-delay profile 32 32 N/A N/A N/A N/A N/A
RMa & RMa O2I Normal-delay profile 37 37 N/A N/A N/A N/A N/A
Long-delay profile 153 153 N/A N/A N/A N/A N/A
Normal-delay profile 240
UMi / UMa O2I
Long-delay profile 616
7.7.4 Spatial filter for generating TDL channel model
The TDL models described in Clause 7.7.2 are generated from the CDL models assuming ideal isotropic antennas at
both Tx and Rx. It is also possible to generate TDL models by assuming non-isotropic antennas like directive horn
antennas or array antennas.
The basic idea to generate a TDL model based on a filtered CDL model is shown in Figure 7.7.4-1 below.
Figure 7.7.4-1 The basic idea for filtering the CDL model to TDL model.
7.7.4.1 Exemplary filters/antenna patterns
Note that any filter/pattern can be applied on a CDL to derive a TDL for evaluating directional algorithms.
Example 1: Isotropic pattern
A{tx,rx} , 1 (7.7-2)
Example 2: Rectangular mask
BW BW
1, 90 &
A{tx,rx} , , BW 2 2 (7.7-3)
0, otherwise.
with BW denotes beamwidth.
Example 3: Simplified antenna pattern given in [ITU-R M.2135]
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Figure 7.7.4-2 Simplified antenna pattern [ITU-R M.2135].
7.7.4.2 Generation procedure
1. The following steps are needed to generate tapped delay line (TDL) models:Choose a CDL model (e.g. CDL-A).
Note that the models may be scaled according to Clause 7.7.5.1 prior to the filtering in order to represent
different angular spreads.
2. Choose spatial filters Atx and Arx defined in LCS
3. Transform the spatial filter into GCS to obtain Atx and Arx such that the pointing direction , is
p p
centered within the filter. The pointing direction may be defined
a. by the dominant path ( P ,P ) (i ,i ) with
i arg max n PnCDL , where P n
CDL
denotes the CDL
cluster power values
b. Or an arbitrary direction
4. Calculate TDL cluster power values PnTDL given the following equation
PnTDL PnCDL Arx n,ZOA , n,AOA Atx n,ZOD , n,AOD (7.7-4)
7.7.5 Extension for MIMO simulations
Extended MIMO link-level channel models can be constructed according to two alternative methods described in the
following.
7.7.5.1 CDL extension: Scaling of angles
The angle values of CDL models are fixed, which is not very suitable for MIMO simulations for several reasons; The
PMI statistics can become biased, and a fixed precoder may perform better than open-loop and on par with closed-loop
or reciprocity beamforming. Furthermore, a CDL only represents a single channel realization. The predefined angle values
in the CDL models can be generalized by introducing angular translation and scaling. By translation, mean angle can be
changed to , desired and angular spread can be changed by scaling. The translated and scaled ray angles can be obtained
according to the following equation:
n,scaled
ASdesired
n,model ,model ,desired (7.7-5)
ASmodel
in which:
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n ,model is the tabulated CDL ray angle
ASmodel is the rms angular spread of the tabulated CDL including the offset ray angles, calculated using the
angular spread definition in Annex A
,model is the mean angle of the tabulated CDL, calculated using the definition in Annex A
,desired is the desired mean angle
ASdesired is the desired rms angular spread
n ,scaled is the resulting scaled ray angle.
The angular scaling is applied on the ray angles including offsets from the tabulated cluster angles. Typical angular
spreads for different scenarios can be obtained from the system-level model.
Example scaling values are:
- AOD spread (ASD) for each CDL model: {5, 10, 15, 25} degrees.
- AOA spread (ASA) for each CDL model: {30, 45, 60} degrees.
- ZOA spread (ZSA) for each CDL model: {5, 10, 15} degrees.
- ZOD spread (ZSD) for each CDL model: {1, 3, 5} degrees.
The angular scaling and translation can be applied to some or all of the azimuth and zenith angles of departure and arrival.
Note: The azimuth angles may need to be wrapped around to be within [0, 360] degrees, while the zenith angles may need
to be clipped to be within [0, 180] degrees.
7.7.5.2 TDL extension: Applying a correlation matrix
The TDLs and the spatial-filtered TDLs can be used with the correlation matrices for MIMO link-level simulations.
Typical correlation parameters can be derived from 1) delay & angular scaled CDLs with antenna array assumptions, 2)
system-level model with antenna array assumptions, or 3) by selecting extreme cases, e.g. uncorrelated, highly
correlated etc. For example, these following options can be considered:
1) Zero correlation (IID channel coefficients) can be used for any number of antenna elements
2) The correlation matrix construction method from TS36.101/104 [15][16] can be used for linear and planar
(single- or dual-polarized) arrays.
- Other correlation parameters α, β, γ than those specified in TS36.101/104 [15][16] and extensions to larger
antenna arrays can be considered. For typical scenarios, α and β will be in the range 0-1
- A representative set of values is {0,0.7,0.9,0.99}
Note: This approach can be applied to TDLs derived from spatially filtered CDLs to emulate hybrid BF system
Note: Other methodologies could also be developed, e.g.
- extending the TS36.101/104 [15][16] procedure to planar arrays or more elements
- using CDLs in combination with array assumptions to derive per-tap correlation matrices as in [17].
- using the system-level model in combination with array assumptions to derive per-tap or per-channel
correlation matrices
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7.7.6 K-factor for LOS channel models
For the LOS channel models of CDL/TDL-D and CDL/TDL-E, the K-factor values may be changed by the user. Mean
and standard deviation of K-factor values can be found in Table 7.5-6, although other values may also be used.
If the K-factor of a model shall be changed to K desired [dB], the cluster powers for the Laplacian clusters (in case of
CDL) or the tap powers for the Rayleigh fading taps (in case of TDL) are determined by
Pn,scaled Pn,model K desired K model , (7.7.6-1)
where Pn ,scaled and Pn ,model n
denote the scaled and the model path power (as given in the tables) of tap/cluster . The
model's K-factor K model is defined as
N Pn,model 10
K model P1,LOS
model 10 log 10 10 . (7.7.6-2)
n1
After scaling the powers, the delay spread needs to be re-normalized. This is done through the two steps below.
1) Calculate the actual RMS delay spread after the K-factor adjustment.
2) Divide the delays by that value to obtain DS = 1.
7.8 Channel model calibration
7.8.1 Large scale calibration
For large scale calibration, fast fading is not modeled. The calibration parameters can be found in Table 7.8-1. The
calibration results based on TR 38.900 V14.0.0 can be found in R1-165974.
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Table 7.8-1: Simulation assumptions for large scale calibration
Parameter Values
Scenarios UMa, UMi-Street Canyon, Indoor-office (open office)
Sectorization 3 sectors per cell site: 30, 150 and 270 degrees
BS antenna configurations Mg = Ng = 1; (M,N,P) = (10, 1, 1), dV = 0.5λ
BS port mapping The 10 elements are mapped to a single CRS port
102 degrees for UMa and UMi Street Canyon
BS antenna electrical downtilting
110 degrees for indoor
DFT precoding according to TR 36.897 with application of panning and tilting
Antenna virtualization
angles
44 dBm for UMi-Street Canyon, 49 for UMa at 6GHz
BS Tx power 35 dBm at 30GHz and 70 GHz for UMa and UMi-Street canyon
24 dBm for Indoor for all carrier frequencies
Bandwidth 20MHz for 6GHz, and 100MHz for 30GHz and 70 GHz
UT antenna configurations 1 element (vertically polarized), Isotropic antenna gain pattern
Handover margin (for calibration) 0dB
Following TR36.873 for UMa and UMi, (3D dropping)
UT distribution
uniform dropping for indoor with minimum distance (2D) of 0 m
UT attachment Based on pathloss considering LOS angle
UT noise figure 9 dB
Fast fading channel Fast fading channel is not modelled
O2I penetration loss 50% low loss and 50% high loss
Carrier Frequency 6 GHz, 30 GHz, 70GHz
Wrapping method for UMa and geographical distance based wrapping (mandatory)
UMi radio distance (optional)
Metrics 1) Coupling loss – serving cell (based on LOS pathloss)
2) Geometry (based on LOS pathloss) with and without white noise
7.8.2 Full calibration
The calibration parameters for full calibration including the fast fading modelling can be found in Table 7.8-2.
Unspecified parameters in Table 7.8-2 are the same as those in Table 7.8-1. When P=2, X-pol (+/-45 degree) is used for
BS antenna configuration 1 and X-pol (0/+90 degree) is used for UT antenna configuration. The calibration results
based on TR 38.900 V14.0.0 can be found in R1-165975.
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Table 7.8-2: Simulation assumptions for full calibration
Parameter Values
Scenarios UMa, UMi-street Canyon, Indoor-office (open office)
Carrier Frequency 6 GHz, 30 GHz, 60GHz, 70GHz
Bandwidth 20MHz for 6GHz, and 100MHz for 30GHz, 60 GHz and 70 GHz
44 dBm for UMi-Street Canyon, 49 for UMa at 6GHz
BS Tx power 35 dBm at 30GHz, 60 GHz and 70 GHz for UMa and UMi-Street canyon
24 dBm for Indoor for all carrier frequencies
Config 1: M = 4,N = 4,P = 2, Mg = 1, Ng = 2, dH = dV = 0.5λ, dH,g = dV,g = 2.5λ … calibration
BS antenna
metrics 1), 2), 3) are calibrated
configurations
Config 2: Mg = Ng = 1, M = N = 2, P = 1 … calibration metrics 1), 2), 4) are calibrated
Config 1: all 16 elements for each polarization on each panel are mapped to a single CRS port;
panning angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-
BS port mapping
scale calibrations
Config 2: each antenna element is mapped to one CRS port
UT antenna Mg = Ng = 1, M = N = 1, P = 2
configurations
Following TR36.873 for UMa and UMi, (3D dropping)
UT distribution
uniform dropping for indoor with minimum distance (2D) of 0 m
UT attachment Based on RSRP (formula) from CRS port 0
Polarized antenna
Model-2 in TR36.873
modelling
UT array orientation ΩUT, uniformly distributed on [0,360] degree, ΩUT,= 90 degree, ΩUT, = 0 degree
UT antenna pattern Isotropic
1) Coupling loss – serving cell
2) Wideband SIR before receiver without noise
3) CDF of Delay Spread and Angle Spread (ASD, ZSD, ASA, ZSA) from the serving cell
(according to circular angle spread definition of TR 25.996)
4)
Metrics CDF of largest (1st) PRB singular values (serving cell) at t=0 plotted in 10*log10 scale.
CDF of smallest (2nd) PRB singular values (serving cell) at t=0 plotted in 10*log10 scale.
CDF of the ratio between the largest PRB singular value and the smallest PRB singular value
(serving cell) at t=0 plotted in 10*log10 scale.
Note: The PRB singular values of a PRB are the eigenvalues of the mean covariance matrix in
the PRB.
7.8.3 Calibration of additional features
The calibration parameters for the calibration of oxygen absorption, large bandwidth and large antenna array, spatial
consistency, and blockage can be respectively found in Table 7.8-3, 7.8-4, 7.8-5, and 7.8-6. Unspecified parameters in
these tables are the same as those in Tables 7.8-1 and 7.8-2. When P=2, X-pol (+/-45 degree) is used for BS antenna
configuration 1 and X-pol (0/+90 degree) is used for UT antenna configuration. The calibration results based on TR
38.900 V14.0.0 can be found in R1-1700990.
Table 7.8-3: Simulation assumptions for calibration for oxygen absorption
Parameter Values
Scenarios UMi-street Canyon
Carrier Frequency 60 GHz
BS antenna
M = 4, N = 4, P = 2, Mg = 1, Ng = 2, dH = dV = 0.5λ, dH,g = dV,g = 2.5λ
configurations
all 16 elements for each polarization on each panel are mapped to a single CRS port; panning
BS port mapping angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-scale
calibrations
Drop multiple users in the multiple cells randomly, and collect the following metrics for each
Calibration method
user after attachment.
1) CDF of coupling loss (serving cell)
Metrics 2) Wideband SINR before receiver – determined from RSRP (formula) from CRS port 0
3) CDF of Delay Spread from the serving cell
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Table 7.8-4: Simulation assumptions for calibration for large bandwidth and large antenna array
Parameter Values
Scenarios UMi-street Canyon
Carrier Frequency 30GHz
Bandwidth 2GHz
BS antenna
M = 8, N = 8, P = 2, Mg = 1, Ng = 4, dH = dV = 0.5λ, dH,g = dV,g = 4λ
configurations
all 64 elements for each polarization on each panel are mapped to a single CRS port; panning
BS port mapping angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-scale
calibrations
The number of rays
NMPC = 40
per cluster
Drop multiple users in the multiple cells randomly, and collect the following metrics for each
Calibration method
user after attachment.
1) CDF of coupling loss (serving cell)
Metrics 2) Wideband SINR before receiver – determined from RSRP (formula) from CRS port 0
3) CDF of largest (1st) PRB singular values (serving cell) at t=0 plotted in 10*log10 scale
Table 7.8-5: Simulation assumptions for calibration for spatial consistency
Parameter Values
Scenarios UMi-street Canyon
Carrier Frequency 30 GHz
BS antenna
M = 4, N = 4, P = 2, Mg = 1, Ng = 2, dH = dV = 0.5λ, dH,g = dV,g = 2.5λ
configurations
all 16 elements for each polarization on each panel are mapped to a single CRS port; panning
BS port mapping angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-scale
calibrations
Following TR36.873, 3D dropping
uniform dropping for indoor with minimum distance of 0 m
UE distribution
For Config1: 100% UE indoor and in the 1 st floor
For Config2: 100% UE outdoor
Config1: UE is stationary
Mobility
Config2: UE is moving with random direction and fixed speed, e.g., 30 km/h
For Config1:
Drop multiple UEs in a single cell, determine all permutations of pairs of UEs, collect the
variables for each pair and bin them into certain distance ranges, e.g., 1m/2m/10m, to get
Calibration method enough samples. Collect the following metrics 1) –6).
For Config2:
Drop multiple users in the single cell, and collect metric 1)-2) and 7)-9) for each user after
attachment.
1) CDF of coupling loss (serving cell)
2) Wideband SINR before receiver – determined from RSRP (formula) from CRS port 0
3) Cross-correlation coefficient of delay for the third cluster between paired UEs, see note
1
4) Cross-correlation coefficient of AOA for the third cluster between paired UEs
5) Cross-correlation coefficient of LOS/NLOS status between paired UEs
Metrics
6) Cross correlation coefficient of the channel response in Step 11 on the first non-DC
subcarrier in an OFDM symbol on antenna port 0 received on the first UE antenna over
multiple realizations between paired UEs
7) CDF of average varying rate of power for the third cluster, see note 2
8) CDF of average varying rate of delay for the third cluster (ns)
9) CDF of average varying rate of AOA for the third cluster (degree)
NOTE 1 For the UT pair at a certain distance range, the variables collected by two UEs can be denoted as X and Y,
respectively, then the cross-correlation coefficient for real number can be written as [E(XY)-E(X)E(Y)]
/sqrt([E(X^2)-E(X)^2])/ sqrt([E(Y^2)-E(Y)^2]), the cross-correlation coefficient for complex number can be
written as |[E(XY*)-E(X)E(Y)*]/sqrt([E(XX*)-E(X)E(X)*])/sqrt([E(YY*)-E(Y)E(Y)*])|.
NOTE 2 For the average varying rate, we assume the collecting interval, e.g., 100ms, and then get the samples for
a certain UE, the varying rate can be written as the standard variance of the samples per 100ms.
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Table 7.8-6: Simulation assumptions for calibration for blockage
Parameter Values
Scenarios UMi-street Canyon
Carrier Frequency 30 GHz
BS antenna
M = 4, N = 4, P = 2, Mg = 1, Ng = 2, dH = dV = 0.5λ, dH,g = dV,g = 2.5λ
configurations
all 16 elements for each polarization on each panel are mapped to a single CRS port; panning
BS port mapping angles of the two subarrays: (0,0) degs; same downtilt angles as used for the large-scale
calibrations
Calibration method For Model A:
Drop multiple users in the multiple cells, and collect the following metrics 1) – 3) for each user
after attachment. Optional self-blocking feature is made mandatory in the Landscape mode only
for calibration purposes.
For Model B:
Drop a BS in (0,0,30) and a UT in (100,0,1.5),
Jump directly to Step 11 and replace the channel with CDL-E.
Drop a blocking screen of size h=10m, w = 2m in (80,10,1.5)
Move the UT from (100,0,1.5) to (100,20,1.5) in small increments. For each UT position,
translate all the AODs and AOAs of CDL-E such that the specular (LOS path) of CDL-E is
pointing along the direct path between the BS and UT.
Collect metric 4)
1) CDF of coupling loss (serving cell)
2) Wideband SINR before receiver – determined from RSRP (formula) from CRS port 0
Metrics
3) CDF of ASA from the serving cell
4) RSRP as a function of UT position
7.8.4 Calibration of the indoor factory scenario
For the InF, the calibration parameters can be found in Table 7.8-7. The calibration results can be found in R1-1909704.
It should be noted absolute delay model had not been agreed by the deadline, so companies were not able to submit
CDF of first path excess delay for serving cell.
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Table 7.8-7: Simulation assumptions for large scale calibration for the indoor factory scenario
Parameter Values
Scenario InF-SL, InF-DL, InF-SH, InF-DH
Hall size InF-SL: 120x60 m
InF-DL: 300x150 m
InF-SH: 300x150 m
InF-DH: 120x60 m
Room height 10 m
Sectorization None
BS antenna
1 element (vertically polarized), Isotropic antenna gain pattern
configurations
UT antenna
1 element (vertically polarized), Isotropic antenna gain pattern
configurations
Handover margin (for 0dB
calibration)
18 BSs on a square lattice with spacing D, located D/2 from the walls.
- for the small hall (L=120m x W=60m): D=20m
- for the big hall (L=300m x W=150m): D=50m
BS deployment
BS height = 1.5 m for InF-SL and InF-DL
BS-height = 8 m for for InF-SH and InF-DH
uniform dropping for indoor with minimum 2D distance of 1 m
UT distribution
UT height = 1.5 m
UT attachment Based on pathloss
UT noise figure 9 dB
Carrier frequency 3.5 GHz, 28 GHz
Bandwidth 100 MHz
Clutter density: 𝑟 Low clutter density: 20%
High clutter density: 60%
Clutter height: ℎ𝑐 Low clutter density: 2 m
High clutter density: 6 m
Clutter size: 𝑑𝑐𝑙𝑢𝑡𝑡𝑒𝑟 Low clutter density: 10 m
High clutter density: 2 m
Metrics 1) Coupling loss – serving cell
2) Geometry with and without noise
3) CDF of delay spread and angle spread (ASD, ZSD, ASA, ZSA) according to the
definition in Annex A.1
4) CDF of first path excess delay for serving cell
8 Map-based hybrid channel model (Alternative
channel model methodology)
Map-based hybrid model is composed of a deterministic component following, e.g., METIS work [6] and a stochastic
component following mainly the model described in clause 7. The channel model methodology described in this clause
is an alternative to the methodology specified in clause 7, and can be used if:
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- The system performance is desired to be evaluated or predicted with the use of digital map to take into account
the impacts from environmental structures and materials.
The map-based hybrid model defined in this clause is not calibrated and can be used per company basis.
8.1 Coordinate system
The same coordinate system as defined in Clause 7.1 is applied.
8.2 Scenarios
The same scenarios as in Clause 7.2 can be applied.
8.3 Antenna modelling
The same antenna modelling as defined in Clause 7.3 can be applied.
8.4 Channel generation
The radio channels are created using the deterministic ray-tracing upon a digitized map and emulating certain stochastic
components according to the statistic parameters listed in Tables 7.5-6 to 7.5-11 [Note: Not all parameters listed in
these tables are used in hybrid model]. The channel realizations are obtained by a step-wise procedure illustrated in
Figure 8.4-1 and described below. In the following steps, downlink is assumed. For uplink, arrival and departure
parameters have to be swapped.
Configurable number of random clusters
Set network Generate Generate
Set scenario layout correlated large- cluster
Import digital map Antenna scale parameters delays
parameters (DS, AS, K)
LoS/NLoS
Ray-tracing
Deterministic
clusters
Perform
Generate Generate Generate
Generate random Merge
ray angle cluster cluster
XPRs coupling of clusters
offsets angles power
rays
Generate
Draw random
channel
initial phases
coefficient
Figure 8.4-1: Channel coefficient generation procedure
Step-wise procedure:
Step 1: Set environment and import digitized map accordingly
a) Choose scenario. Choose a global coordinate system and define zenith angle θ, azimuth angle ϕ, and spherical
basis vectors ˆ , ˆ as shown in Figure 7.5-2.
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b) Import digitized map according to the chosen scenario. The digitized map should at least contain the following
information:
- The 3D geometric information for each of major structures involving with buildings or rooms. The external
building walls and internal room walls are represented by surfaces and identified by the coordinates of the
vertices on each wall.
- The material and thickness of each wall as well as the corresponding electromagnetic properties including
permittivity and conductivity.
- Random small objects in certain scenarios (e.g, UMi outdoor)
The format of digitized map, including additional information besides above-mentioned, is per implementation wise
and out of scope of this description.
Step 2: Set network layout, and antenna array parameters
a) Give number of BS and UT.
b) Give 3D locations of BS and UT, and calculate LOS AOD (ϕLOS,AOD), LOS ZOD (θLOS,ZOD), LOS AOA (ϕLOS,AOA),
LOS ZOA (θLOS,ZOA) of each BS and UT in the global coordinate system
c) Give BS and UT antenna field patterns Frx and Ftx in the global coordinate system and array geometries
d) Give BS and UT array orientations with respect to the global coordinate system. BS array orientation is defined
by three angles ΩBS,α (BS bearing angle), ΩBS,β (BS downtilt angle) and ΩBS,γ (BS slant angle). UT array
orientation is defined by three angles ΩUT,α (UT bearing angle), ΩUT,β (UT downtilt angle) and ΩUT,γ (UT slant
angle). Give rotational motion of UT in terms of its bearing angle, downtilt angle and slant angle if UT rotation
is modelled.
e) Give speed and direction of motion of UT in the global coordinate system for virtual motion.
f) Give system centre frequency/frequencies and bandwidth(s) for each of BS-UT links
If the bandwidth (denoted as B ) is greater than c/D Hz, where c is the speed of light and D is the maximum antenna
aperture in either azimuth or elevation, the whole bandwidth is split into K B equal-sized frequency bins, where
B
KB is a per-implementation parameter taking into account the channel constancy as well as other
c D
B
potential evaluation needs, and the bandwidth of each frequency bin is B . Within k-th frequency bin,
KB
the channel power attenuation, phase rotation, Doppler are assumed constant, whose corresponding values are
K B 2k 1
calculated based on the centre frequency of k-th frequency bin fk fc B for 1 k KB ,
2
where f c is the centre frequency of the corresponding BS-UT link.
Step 3: Apply ray-tracing to each pair of link ends (i.e., end-to-end propagation between pair of Tx/Rx arrays).
a) Perform geometric calculations in ray-tracing to identify propagation interaction types, including LOS,
reflections, diffractions, penetrations and scattering (in case the digitized map contains random small objects),
for each propagation path. In general, some maximum orders of different interaction types can be set.
- The theoretical principles and procedures of geometric tracing calculations can be found in [6]~[10][12]. This
description does not intend to mandate new concepts and/or procedures to the conventional ray-tracing
algorithms; on the other hand, the implementation-based variations aiming to reduce computation complexity
are allowed within limits of acceptable calibration tolerances.
- The same geometric calculation is shared among all K B frequency bins.
b) Perform electric field calculations over propagation path, based on identified propagation interaction types (LOS,
reflection, diffraction, penetration and scattering) and centre frequencies of frequency bins.
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The details of electric field calculation can be found in [6]~[13].
The modelling algorithms in geometry and electric field calculations for different propagation interactions are
summarized in the table below.
Table 8.4-1 Principles applied in ray-tracing
Geometry calculation Electric field calculation
LOS Free space LOS Friis equation [11]
Reflection Snell's law with image-based method [7] Fresnel equation [7]
Diffraction Fermat's principle [12] UTD [13]
Penetration Snell's law for transmission through slab [8] Fresnel equation [7]
Scattering (upon small Isotropic scattering [6] RCS-based scattering coefficient [6]
objects)
Note: For reasons of simplicity and simulation speed, the maximum order of reflection on a path without
diffraction is configurable from {1,2,3}; the maximum order of diffraction on a path without reflection is
configurable from {1,2}; the path containing both reflection and diffraction has 1-order reflection and 1-
order diffraction, besides any potential penetrations; and the maximum order of penetration on a path is
configurable, with the recommended value equal to 5.
The outputs from Step 3 should at least contain following for each pair of link ends:
- the LOS/NLOS flag to indicate whether a LOS propagation mechanism exists;
- the number of deterministic propagation paths LRT (also referred as deterministic clusters in Step 8. To avoid the
unnecessary computation complexity, these LRT deterministic paths only include those paths whose powers are
higher than 25dB below the maximum deterministic path power, where the path power is denoted as Pl RT ,real
RT
and defined below);
- for each deterministic path ( lRT -th path sorted in ascending order of path delay):
- the flag indicating whether the deterministic path is generated with scattering upon random small objects;
-
RT RT RT
l RT
the normalized path delay lRT l min l and the first arrival absolute delay min
lRT
(with
l RT lRT to
be the real absolute propagation delay of the path);
- angles of arrival and departure [ lRT, AOA , lRT
RT , ZOA
, lRT
RT , AOD
, lRT,ZOD ];
RT RT
1 K B RT , real
- the power Pl RT,k,real for k-th frequency bin, and the path power
RT
Pl RTRT , real Pl ,k
K B k 1 RT
1 K B RT
- the XPR lRT of the path, where lRT
RT RT
l , k with lRTRT ,k being the XPR for k-th frequency bin.
K B k 1 RT
- to support for true motion, i.e. the case when a trajectory is specified for UT, a path ID is associated for each
deterministic path. The same ID is associated for a path across a number of UT locations as far as 1) it has
same interaction types in the same order and 2) its interactions occur in same walls or other surfaces.
The LRT deterministic paths are sorted by normalized path delay ( lRT
RT
) in ascending order. That is to say, 1RT =
0.
If LRT =0 for a pair of link ends, the channel gain for this pair of link ends is assumed to be zero and the
remaining steps are skipped with none of random cluster.
Step 4: Generate large scale parameters e.g. delay spread, angular spreads and Ricean K factor for random clusters.
The generation of large scale parameters takes into account cross correlation according to Table 7.5-6 and uses
the procedure described in Clause 3.3.1 of [14] with the square root matrix CMxM (0) being generated using the
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Cholesky decomposition and the following order of the large scale parameter vector: sM = [sK, sDS, sASD, sASA, sZSD,
sZSA]T. Limit random RMS azimuth arrival and azimuth departure spread values to 104 degrees, i.e., ASA=
min(ASA ,104), ASD = min(ASD ,104). Limit random RMS zenith arrival and zenith departure spread values
to 52 degrees, i.e., ZSA = min(ZSA,52), ZSD = min(ZSD,52). For the parameter selection from Table 7.5-6,
the LOS/NLOS condition determined in Step 3 is applied.
Step 5: Generate delays (denoted as {
RC
) for random clusters
Delays are drawn randomly according to the exponential delay distribution
n RC ln X n (8.4-1)
1 LRT
where RC
max ,
LRT
RT
, Xn ~ uniform(0,1), and cluster index n = 0,…, LRC
l RT
to be
with LRC
l RT 1
configurable. A recommended value for LRC is the number of clusters given in Table 7.5-6.
LRT 1 LRT
r DS
LRC
r DS
1 LRT
RT
l RT
, where rτ is the delay distribution proportionality factor given
l RT 1
in Table 7.5-6.
Normalise the delays by subtracting the minimum delay and sort the normalised delays to ascending order:
n sort n min n C (8.4-2)
where C is the additional scaling of delays to compensate for the effect of LOS peak addition to the delay
spread, and is depending on the heuristically determined Ricean K-factor [dB] as generated in Step 4:
0.7705 0.0433K 0.0002K 2 0.000017 K 3 LOS condition
C (8.4-3)
1 NLOS condition
For the delay used in cluster power generation in Step 6, the scaling factor C is always 1.
The n-th random cluster is removed if n=0 or n lRT th for any of 1≤lRT≤LRT, where τth is given by
RT
1
th RC ln , and p0 is the configurable probability for cluster inter-arrival interval to be less than
1 p0
τth. For example, set p0 =0.2 to obtain τth=0.223 RC .
Denote nRC for 1≤n≤LRC as the delays of the LRC random clusters that remain after the cluster removal.
As an add-on feature, the absolute time of arrival of a cluster is derived as:
- lRT
, if the cluster is the lRT -th deterministic path obtained in Step 3 for 1≤lRT≤LRT;
min lRT + n , if the cluster is the n-th random cluster for 1≤n≤LRC.
RC
-
lRT
Step 6: Generate powers (denoted as Pi RC ,real for 1≤i≤LRC) for random clusters.
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Cluster powers for the random clusters are calculated assuming a single slope exponential power delay profile.
First, the virtual powers (denoted as Pi RC ,virtual for 1≤i≤LRC) of random clusters and virtual powers (denoted as
PjRT ,virtual for 1≤j≤LRT) of deterministic clusters are calculated as following.
Denote:
i , RC
r 1
Vi RC
exp iRC 10 10
r DS
(8.4-4)
j , RT
r 1
V j
RT
exp RT
j 10 10
(8.4-5)
r DS
where i,RC and j,RT are the per cluster shadowing terms in [dB] and meet distribution of N(0,). Then,
1 Vi RC
Pi RC ,virtual (8.4-6)
A 1 LRC LRT
V
i 1
i
RC
V
j 1
j
RT
V jRT
j 1
1 A
PjRT ,virtual (8.4-7)
A 1 LRC LRT
A 1
V
i 1
i
RC
V
j 1
j
RT
In the case of LOS condition, A=KR with KR being the Ricean K-factor obtained in Step 4 and converted to linear
scale; otherwise, A=0. The real power (including effects of pathloss) per random cluster in k-th frequency bin is
given by
LRT
P
j 1
RT , real
j ,k
Pi ,RC
k
, real
LRT
Pi RC ,virtual (8.4-8)
P j 1
j
RT ,virtual
for 1≤i≤LRC and 1 k KB . Similar to path power of deterministic cluster, the path power of i-th random
cluster is calculated as
1 K B RC , real
Pi RC , real Pi,k .
K B k 1
(8.4-9)
Step 7: Generate arrival angles and departure angles for both azimuth and elevation, for each random cluster.
For azimuth angles of the n-th random cluster:
The composite PAS in azimuth of all random clusters is modelled as wrapped Gaussian (see Table 7.5-6). The
AOAs are determined by applying the inverse Gaussian function with input parameters PnRC ,real and RMS angle
spread ASA
2(ASA / 1.4) ln PnRC ,real max Pi RC ,real , PjRT ,real
i, j
n, AOA (8.4-10)
C
with constant C defined as
3GPP
Release 17 91 3GPP TR 38.901 V17.0.0 (2022-03)
C NLOS
CNLOS 1.1035 0.028K 0.002 K 2 0.0001K 3 , for LOS
(8.4-11)
C , for NLOS
where CNLOS is defined as a scaling factor related to the total number of clusters and is given in Table 7.5-2.
In the LOS case, constant C also depends on the Ricean K-factor K in [dB], as generated in Step 4. Additional
scaling of the angles is required to compensate for the effect of LOS peak addition to the angle spread.
Assign positive or negative sign to the angles by multiplying with a random variable Xn with uniform
distribution to the discrete set of {1,–1}, and add component Yn ~ N 0, ASA 7 2
to introduce random
variation
n, AOA X nn, AOA Yn center, AOA (8.4-12)
where center, AOA is calculated as
LRT
center, AOA arg Pl RT ,real exp jlRT
, AOA
(8.4-13)
l 1
Note that l,RTAOA shall be given in radians here.
The generation of AOD (n,AOD) follows a procedure similar to AOA as described above.
For zenith angles of the n-th random cluster:
The generation of ZOA assumes that the composite PAS in the zenith dimension of all random clusters is
Laplacian (see Table 7.5-6). The ZOAs are determined by applying the inverse Laplacian function with input
parameters PnRC ,real and RMS angle spread ZSA
ZSA ln PnRC ,real max Pi RC ,real , PjRT ,real
n, ZOA
i, j
(8.4-14)
C
with C defined as
C NLOS
C NLOS 1.3086 0.0339K 0.0077 K 2 0.0002K 3 , for LOS
, (8.4-15)
C , for NLOS
where CNLOS is a scaling factor related to the total number of clusters and is given in Table 7.5-4.
In the LOS case, constant C also depends on the Ricean K-factor K in [dB], as generated in Step 4. Additional
scaling of the angles is required to compensate for the effect of LOS peak addition to the angle spread.
Assign positive or negative sign to the angles by multiplying with a random variable Xn with uniform
distribution to the discrete set of {1,–1}, and add component Yn ~ N 0, ZSA 7 2
to introduce random
variation
n,ZOA X n n,ZOA Yn ZOA (8.4-16)
3GPP
Release 17 92 3GPP TR 38.901 V17.0.0 (2022-03)
where ZOA 900 if the UT is located indoors and ZOA center,ZOA if the UT is located outdoors, where
center, ZOA is calculated as
LRT
center,ZOA arg Pl RT ,real exp j lRT
, ZOA
(8.4-17)
l 1
Note that l,RTZOA shall be given in radians here.
The generation of ZOD follows the same procedure as ZOA described above except equation (8.4-16) is
replaced by
n, ZOD X n n, ZOD Yn center, ZOD offset, ZOD (8.4-18)
where variable Xn is with uniform distribution to the discrete set of {1,–1}, Yn ~ N 0, ZSD 7 , 2
offset, ZOD
is given in Tables 7.5-7 to 7.5-11.
Step 8: Merge deterministic clusters and random clusters.
First, remove any deterministic or random cluster with less than -25 dB power compared to max{ PjRT ,real ,
Pi RC ,real } for all 1≤j≤LRT and 1≤i≤LRC. Then, simply put the remaining deterministic clusters and random
clusters into single set of clusters, and meanwhile maintain an attribute for each cluster to indicate whether the
cluster is a deterministic cluster or a random cluster.
Step 9: Generate ray delays and ray angle offsets inside each cluster, where the cluster can be either random or
deterministic.
Denote M as the number of rays per cluster, where M=1 if the cluster corresponds to n=1 in the LOS case,
otherwise the value of M is given in Table 7.5-6.
When KB 1:
The relative delay of m-th ray within n-th cluster is given by n , m 0 for m = 1,…,M.
The azimuth angle of arrival (AOA) for the m-th ray in n-th cluster is given by
n ,m, AOA n , AOA c ASA m (8.4-19)
where c ASA is the cluster-wise rms azimuth spread of arrival angles (cluster ASA) in Table 7.5-6, and offset
angle m is given in Table 7.5-3. n, AOA equals to the AOA angle output from Step 3 if n-th cluster is
deterministic cluster, and equals to the AOA angle (8.4-12) in Step 7 if n-th cluster is random cluster.
The generation of AOD (n,m,AOD) follows a procedure similar to AOA as described above.
The zenith angle of arrival (ZOA) for the m-th ray in n-th cluster is given by
n ,m,ZOA n ,ZOA cZSA m (8.4-20)
where cZSA is the cluster-wise rms spread of ZOA (cluster ZOA) in Table 7.5-6, and offset angle m is given in
Table 7.5-3. Assuming that n , m , ZOA is wrapped within [0, 360°], if n ,m , ZOA [180,360] , then n ,m ,ZOA is
set to (360 n ,m,ZOA ) . n,ZOA equals to the ZOA angle output from Step 3 if n-th cluster is deterministic
cluster, and equals to the ZOA angle (8.4-16) in Step 7 if n-th cluster is random cluster.
The zenith angle of departure (ZOD) for the m-th ray in n-th cluster is given by
3GPP
Release 17 93 3GPP TR 38.901 V17.0.0 (2022-03)
lgZSD
n,m,ZOD n,ZOD (3 / 8)(10 ) m (8.4-21)
where lgZSD is the mean of the ZSD log-normal distribution. n,ZOD equals to the ZOD angle output from
Step 3 if n-th cluster is deterministic cluster, and equals to the ZOD angle (8.4-18) in Step 7 if n-th cluster is
random cluster.
When KB 1:
The relative delay of m-th ray within n-th cluster is given by n , m sort n, m min
1 m M
that are sorted in
n, m
ascending order, where n,m ~ unif 0,2cDS , with the cluster delay spread as given in Table 7.5-6.
unif a, b denotes the continuous uniform distribution on the interval a, b . Note that n, m shall be the
independently generated.
The azimuth angles (AOA and AOD) and zenith angles (ZOA and ZOD) for the m-th ray in n-th cluster in each
frequency bin is given by
n ,m , AOA n , AOA n,m , AOA
n ,m , AOD n , AOD n,m , AOD
(8.4-22)
n ,m , ZOA n , ZOA n,m , ZOA
n ,m , ZOD n , ZOD n,m , ZOD
for m = 1,…,M, where n,{ AOA| AOD} and n ,{ZOA| ZOD} equal to the {AOA,AOD} and {ZOA, ZOD} angle
outputs from Step 3 if n-th cluster is deterministic cluster, and equal to the {AOA,AOD} and {ZOA, ZOD}
angle in Step 7 if n-th cluster is random cluster; and
n, m, AOA ~ 2cASA unif -1,1
n, m, AOD ~ 2cASDunif -1,1
n, m, ZOA ~ 2cZSA unif -1,1
(8.4-23)
n, m, ZOD ~ 2cZSD unif -1,1
with the respective cluster angular spreads as given in Tables 7.5-6 to 7.5-11.
Assuming that n ,m , ZOA is wrapped within [0, 360°], if n,m,ZOA [180,360] , then n ,m ,ZOA is set to
(360 n ,m,ZOA ) .
Step 10: Generate power of rays in each cluster, where coupling of rays within a cluster for both azimuth and elevation
could be needed.
Given Pn , k as the real power in k-th frequency bin for the n-th cluster (either deterministic or random) obtained
from Step 8,
When KB 1:
Couple randomly AOD angles n,m,AOD to AOA angles n,m,AOA within a cluster n. Couple randomly ZOD angles
n ,m ,ZOD with ZOA angles n ,m ,ZOA using the same procedure. Couple randomly AOD angles n,m,AOD with
ZOD angles n ,m ,ZOD within a cluster n.
The power of m-th ray in n-th cluster and in k-th frequency bin is given by Pn ,m ,k Pn ,k M for m = 1,…,M.
3GPP
Release 17 94 3GPP TR 38.901 V17.0.0 (2022-03)
When KB 1:
Pn,m
The power of m-th ray in n-th cluster and in k-th frequency bin is given by Pn ,m ,k Pn ,k M
for
P
m 1
n,m
m = 1,…,M, where
2 n,m , AOA 2 n,m , AOD
Pn,m exp n ,m exp exp
c DS cASA cASD
(8.4-24)
2 n,m ,ZOA 2 n,m ,ZOD
exp exp
cZSA cZSD
and cDS , cASA , cASD , and cZSA are respectively the intra-cluster delay spread and the corresponding intra-
cluster angular spreads that are given in Table 7.5-6. The cluster zenith spread of departure is given by
3
cZSD 10 lgZSD , (8.4-25)
8
with lgZSD being defined in Tables 7.5-7, 7.5-8, 7.5-9, 7.5-10 and 7.5-11.
Step 11: Generate XPRs
Generate the cross polarization power ratios (XPR) for each ray m of each cluster n. XPR is log-Normal
distributed. Draw XPR values as
n, m 10 X n ,m / 10
(8.4-26)
where X n,m ~ N (XPR , XPR
2
) is Gaussian distributed with XPR given from Table 7.5-6. If n-th cluster is a
deterministic cluster, 10 log 10 RT
lRT ; otherwise, XPR is given in Table 7.5-6.
Note: X n , m is independently drawn for each ray and each cluster.
Step 12: Draw initial random phases
Draw random initial phase
n,m , n,m , n,m , n,m for each ray m of each cluster n and for four different
polarisation combinations (θθ, θϕ, ϕθ, ϕϕ). The distribution for initial phases is uniform within (-).
In the LOS case, calculate an initial phase LOS 2 d3D 0 for both θθ and ϕϕ polarisations, where d3D
is the 3D distance between transmitter and receiver and λ0=c/fc is the wavelength of the modelled propagation
link.
Step 13: Generate channel coefficients for each cluster n and each receiver and transmitter element pair u, s.
In case of NLOS, the channel coefficients of ray m in cluster n for a link between Rx antenna u and Tx antenna s
at time t in k-th frequency bin can be calculated as
3GPP
Release 17 95 3GPP TR 38.901 V17.0.0 (2022-03)
Frx,u , n ,m ,ZOA , n ,m , AOA
H u ,s ,n ,m ,k t
T
exp j
n ,m n,1m exp j
n ,m
Frx,u , n ,m ,ZOA , n ,m , AOA
n,1m exp j n ,m
exp j
n ,m
Ftx,s , n ,m ,ZOD , n ,m , AOD
exp j 2
fk T
rˆrx,n ,m .d rx,u rˆtxT,n ,m .d tx,s
(8.4-27)
F ,
tx,s , n ,m ,ZOD n ,m , AOD c
OLn , m f k BLn , m f k ,t
Pn ,m ,k 10 20 exp j 2 f k rˆrxT ,n ,m .v t
c
where Frx,u,θ and Frx,u,ϕ are the receive antenna element u field patterns in the direction of the spherical basis
vectors, ˆ and ˆ respectively, Ftx,s,θ and Ftx,s,ϕ are the transmit antenna element s field patterns in the direction
of the spherical basis vectors, ˆ and ˆ respectively. The delay (TOA) for ray m in cluster n for a link between
Rx antenna u and Tx antenna s is given by:
u ,s,n,m n n ,m 1c rˆrxT ,n,m .drx,u 1c rˆtxT,n,m .dtx,s (8.4-28)
For the m-th ray within n-th cluster, rˆrx, n , m is the spherical unit vector with azimuth arrival angle n ,m, AOA and
elevation arrival angle n ,m , ZOA , given by
sin n ,m ,ZOA cos n ,m , AOA
rˆrx,n ,m sin n ,m ,ZOA sin n ,m , AOA (8.4-29)
cos n ,m ,ZOA
rˆtx, n , m is the spherical unit vector with azimuth departure angle n , m , AOD and elevation departure angle
n ,m , ZOD , given by
sin n ,m ,ZOD cos n ,m , AOD
rˆtx,n ,m sin n ,m ,ZOD sin n ,m , AOD (8.4-30)
cos n ,m ,ZOD
Also, d rx,u is the location vector of receive antenna element u and d tx,s is the location vector of transmit antenna
element s, n,m is the cross polarisation power ratio in linear scale. If polarisation is not considered, the 2x2
polarisation matrix can be replaced by the scalar exp jn,m and only vertically polarised field patterns are
applied.
The Doppler frequency component is calculated from the arrival angles (AOA, ZOA), and the UT velocity
vector v with speed v, travel azimuth angle ϕv, elevation angle θv and is given by
v v.sin v cos v sin v sin v cosv ,
T
(8.4-31)
In case of LOS, the channel coefficient is calculated in the same way as in (8.4-27) except for n=1:
Frx,u , LOS ,ZOA , LOS , AOA exp j LOS Ftx,s , LOS ,ZOD , LOS , AOD
T
0
H u ,s ,n1,k t
Frx,u , LOS ,ZOA , LOS , AOA exp j LOS Ftx,s , LOS ,ZOD , LOS , AOD
0 (8.4-32)
OLn , m 1 f k BLn , m 1 f k ,t
f
exp j 2 k rˆrxT ,LOS .d rx,u rˆtxT,LOS .d tx,s . P1,k 10
20 exp j 2 f k rˆrxT ,LOS .v t
c c
where the corresponding delay (TOA) for cluster n=1 for a link between Rx antenna u and Tx antenna s is
given by u , s ,n1 n 1c rˆrxT , LOS .d rx,u 1c rˆtxT, LOS .dtx, s .
3GPP
Release 17 96 3GPP TR 38.901 V17.0.0 (2022-03)
In (8.4-27) and (8.4-32), the oxygen absorption loss, OLn,m(f), for each ray m in cluster n at carrier frequency f is
modelled as
OLn,m(f) = α(f)/1000 · c ·
min [dB]
n n, m l RT l RT
(8.4-33)
where:
- α(f) is the frequency dependent oxygen loss per distance (dB/km) characterized in Clause 7.6.1;
- c is speed of light (m/s); and
- n is the delay (s) obtained from Step 3 for deterministic clusters and from Step 5 for random clusters.
min lRT is from the output of Step 3.
lRT
In (8.4-27) and (8.4-32), blockage modelling is an add-on feature. If the blockage model is applied, the blockage
loss, BLn,m(f,t) in unit of dB, for each ray m in cluster n at carrier frequency f and time t is modelled in the same
way as given in Clause 7.6.4; otherwise BLn,m(f,t)=0dB for all f and t.
3GPP
Release 17 97 3GPP TR 38.901 V17.0.0 (2022-03)
Annex A:
Further parameter definitions
A.1 Calculation of angular spread
Based on the circular standard deviation in directional statistics, the following expression for the angular spread AS in
radians is proposed
N M
exp j P n ,m n ,m
AS 2 ln n 1 m 1 (A-1)
N M
P n,m
n 1 m 1
where Pn ,m is the power for the mth subpath of the nth path and n, m is the subpaths angle (either AOA, AOD, ZOA,
ZOD) given in radians.
A.2 Calculation of mean angle
The power weighted mean angle is given by
arg exp jn, m Pn, m
N M
(A-2)
n 1 m 1
where Pn ,m is the power for the mth subpath of the nth path and n, m is the subpaths angle (either AOA, AOD, ZOA,
ZOD) given in radians.
3GPP
Release 17 98 3GPP TR 38.901 V17.0.0 (2022-03)
Annex B: Change history
Change history
Date Meeting TDoc CR Rev Cat Subject/Comment New
version
2017-02 RAN1#88 R1-1703641 Skeleton TR based on TR 38.900 v14.2.0, adding new changes 0.0.1
(from R1-1701231, R1-1701406, R1-1701410, R1-1701411, R1-
1701412, R1-1701413, R1-1701414, R1-1701416, R1-1701417 and
R1-1701425) to have a TR applicable for the frequency range 0.5 to
100 GHz
2017-02 RAN1#88 R1-1704145 Adding new agreements (from R1-1702701, R1-1703635, R1- 0.1.0
1703873, R1-1703358, R1-1702703, R1-1703637, R1-1703354, R1-
1703458, R1-1703647, R1-1701936) and reflecting comments from
RAN1 e-mail discussions [88-17].
2017-02 RAN#75 RP-170394 Submitted to RAN for information 1.0.0
2017-03 RAN#75 RP-170737 Add TR number further to the approval of revised SID in RP-170379. 1.0.1
Submitted to RAN for one step approval
2017-03 RAN#75 MCC clean-up – Further to RAN#75 decision, TR goes under 14.0.0
change control as Rel-14
2017-06 RAN#76 RP-171208 0001 - F Correction on site specific effective height for TR38.901 14.1.0
2017-06 RAN#76 RP-171208 0003 - F Correction for LOS probability 14.1.0
2017-06 RAN#76 RP-171208 0004 - F Correction for scaling factor for ZOD generation 14.1.0
2017-06 RAN#76 RP-171208 0005 - F Correction for modelling of intra-cluster angular and delay spreads 14.1.0
2017-06 RAN#76 RP-171208 0007 - F TR38.901_CR_LCS_Radiation_Field_Notation 14.1.0
2017-06 RAN#76 RP-171208 0008 - F TR38.901_CR_Mean_angle_definition 14.1.0
2017-06 RAN#76 RP-171208 0009 - F TR38.901_CR_O2I_penetration_loss 14.1.0
2017-06 RAN#76 RP-171208 0010 1 F TR38.901_CR_Correction_on_Rural_LSP 14.1.0
2017-06 RAN#76 RP-171208 0011 1 F Clarification for spatial consistency procedure 14.1.0
2017-06 RAN#76 RP-171208 0013 - F TR38.901_CR_Scenarios_in_Simulation_Assumptions 14.1.0
2017-06 RAN#76 RP-171208 0014 - F TR38.901_CR_TDL_Spatial_Filter 14.1.0
2017-06 RAN#76 RP-171208 0015 - F Correction of spatial-consistency UE mobility modelling Procedure A 14.1.0
2017-07 MCC: UMa LOS probability in Table 7.4-2 is made fully visible 14.1.1
2017-09 RAN#77 RP-171648 0016 - F Correction on the formula of Annex A 14.2.0
2017-09 RAN#77 RP-171648 0018 2 F Correction on cross polarization power ratios in TR 38.901 14.2.0
2017-09 RAN#77 RP-171648 0019 1 F TR38.901_CR_Spatially-consistent_UT_modelling 14.2.0
2017-09 RAN#77 RP-171648 0020 1 F TR38.901_CR_Calibraiton_Results 14.2.0
2017-12 RAN#78 RP-172687 0022 - F Correction on 3D-InH channel model in TR 38.901 14.3.0
2018-06 SA#80 - - - - Update to Rel-15 version (MCC) 15.0.0
2019-09 RAN#85 RP-191944 0023 - B Addition of indoor industrial channel model – version created in error 15.1.0
– withdrawn
2019-10 RAN#85 RP-191944 0023 - B Addition of indoor industrial channel model – creation of rel-16 report 16.0.0
2019-12 RAN#86 RP-192629 0024 - F CR to TR 38.901 for remaining open issues in IIOT channel 16.1.0
modelling
2022-03 SA#95-e Update to Rel-17 version (MCC) 17.0.0
3GPP
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