5G and IoT technologies

By Mrs.Talekar Rohini
Assistant Professor
CSO department
 Unit 2
• Syllabus:
-The 5G wireless Propagation Channels: Channel modeling
requirements, propagation scenarios and challenges in the 5G
modeling, Channel Models for mmWave MIMO Systems, 3GPP
standards for 5G, IEEE 802.15.4
Propagation scenarios and challenges in the
5G modelling
• Propagation :
Tendency of EM wave to travel from one place to
another place i.e from transmitter(base station) to
Receiver(user device).
When a signal hits the obstacles ,3 possible effects
will happen based on surface it hits -
1) Reflection
2) Diffraction
3) Scattering
Different Propagation scenarios:
• Propagation scenarios in 5G modeling refer to specific
environments or conditions in which radio frequency
signals propagate through the wireless medium.
• Accurate modeling of propagation scenarios is
crucial for designing and optimizing 5G networks.
• Here are the key propagation scenarios in 5G modeling:
Propagation Scenarios:
1) Free Space Propagation
2) Urban Micro-Cell (UMi) Propagation
3) Urban Macro-Cell (UMa) Propagation
4) Rural Macro-Cell (RMa) Propagation
5) Indoor Propagation
6) Tunnel Propagation
7) Vehicular Propagation
8) Massive MIMO (Multiple-Input Multiple-Output) Propagation
9) mmWave Propagation
10) Non-Line-of-Sight (NLOS) Propagation
1)Free Space Propagation
• In this scenario, signals propagate in an open
space without any obstructions or reflections.
• It serves as a baseline for understanding
signal attenuation over distance.
2)Urban Micro-Cell (UMi) Propagation

• UMi propagation models communication in urban

environments with small cells.
• Includes scenarios where small cells are deployed
in dense urban areas, such as city centers,
to enhance capacity and coverage.
3)Urban Macro-Cell (UMa) Propagation

• UMa propagation models communication in

urban environments with large cells (macro-cells).
• Represents scenarios where macro-cells
cover broader areas within urban settings.
4) Rural Macro-Cell (RMa) Propagation
• RMa propagation models communication in rural
or suburban areas with large cells (macro-cells).
• Reflects scenarios where macro-cells provide
coverage in less densely populated regions.
5) Indoor Propagation

• Models signal propagation within buildings,

shopping malls, airports, and other indoor environments.
• Takes into account factors like wall penetration,
reflection, and diffraction for accurate signal modeling.
• Scenario: Communication within buildings, stadiums, shopping
malls, etc.
• Challenges: Modeling signal penetration through walls, floors,
and ceilings, as well as reflections and multipath effects in
confined spaces.
6) Tunnel Propagation

• Models communication in tunnels, underground railways,

or similar enclosed spaces.
• Takes into consideration the unique challenges of
signal propagation in confined and obstructed environments.
7) Vehicular Propagation

• Models communication between vehicles (V2V)

and between vehicles and infrastructure (V2I).
• Considers high mobility, fast-changing propagation conditions,
and potential obstructions.
• Scenario: Communication between vehicles and infrastructure
or high-speed mobility scenarios (e.g., trains, planes).
• Challenges: Modeling Doppler shifts, rapid channel variations,
and handovers at high speeds, ensuring seamless connectivity
and low-latency communication.
8) Massive MIMO (Multiple-Input Multiple-
Output) Propagation
• Models scenarios where a large number of antennas
are used at the base station (BS) or access point.
• Considers beamforming, spatial multiplexing,
and interference management in massive MIMO systems.
• Scenario: Utilizing a large number of antennas at both the
transmitter and receiver for increased capacity and spectral
efficiency.
• Challenges: Modeling the complex interactions, beamforming,
and interference mitigation in massive MIMO systems
accurately.
9) mmWave Propagation

• Models communication using

millimeter-wave frequencies (e.g., 24-100 GHz).
• Takes into account high propagation losses,
atmospheric absorption, and environmental impact on signal
strength.
• Scenario: Utilizing high-frequency bands (e.g., 24-100 GHz) for
high data rates.
• Challenges: mmWave signals are highly susceptible to
absorption, rain fade, and atmospheric gases, requiring precise
modeling of path loss, reflections, and diffraction.
10) Non-Line-of-Sight (NLOS)
Propagation
• Models scenarios where there is no direct
line of sight between the transmitter and receiver.
• Accounts for multipath propagation, scattering,
and reflections to predict received signal characteristics.
• Scenario: Radio wave propagation where there is no direct line
of sight between the transmitter and receiver.
• Challenges: Accurately modeling multipath propagation,
scattering, and reflections to account for signal degradation and
optimize receiver algorithms.
Channel Models for mmWave MIMO Systems:
• Millimeter-wave (mmWave) MIMO (Multiple-Input, Multiple-
Output) systems are a key technology in 5G and beyond,
offering high data rates and massive capacity.
• The mmWave frequencies typically range from 30 GHz to 300
GHz.
• Channel models for mmWave MIMO systems are crucial for
simulating and understanding the behavior of the wireless
communication channel at these frequencies.
• It's important to choose an appropriate channel model based on
the specific use case, frequency band, environment, and
system requirements.
Here are some commonly used channel
models for mmWave MIMO systems:
1) Geometric Channel Model (GCM)
-The GCM is based on geometric optics and models the
propagation of electromagnetic waves based on ray tracing.
-It considers reflections, diffractions, and scattering
to estimate the channel characteristics, such as
path loss, delay spread, and angular spread.
2) Saleh-Valenzuela (SV) Model:
-The SV model is a statistical model that describes the
angular characteristics of mmWave channels.
-It incorporates a clustered structure to represent
the angular spread of arrival and departure directions.
3) 3GPP 3D Channel Model:
-The 3rd Generation Partnership Project (3GPP)
provides standardized channel models for both
sub-6 GHz and mmWave frequencies.
-The 3D channel model is widely used for
mmWave MIMO systems, incorporating aspects
like path loss, shadowing, and angular spread.
4) Quasi-Deterministic Channel Models
-These models combine aspects of deterministic and
statistical approaches to describe the channel.
-They use a deterministic description of some paths
while incorporating statistical models for others.
5) Hybrid Channel Model:
-This model combines aspects of both deterministic
and stochastic channel models.
- It often uses a geometric channel model for the
spatial domain and a statistical model for the
time and frequency domains.
6)Ray-Tracing Models:
-Ray tracing techniques are used to model the
propagation environment, considering reflections,
refractions, and diffractions.
-These models provide detailed channel responses
by considering the physical geometry of the environment.
7) Statistical Spatial Channel Models (SSCM):
-SSCM defines statistical models for spatial parameters
like angle of arrival (AoA), angle of departure (AoD),
and delay spread based on measurements and statistical
analysis.
8) Cluster-Based Models:
-These models represent the channel as a collection of clusters,
where each cluster comprises multiple rays with
similar propagation characteristics.
-Clusters are often used to model the dominant multipath
components.
9) Double-Directional Clustered Channel Model:
This model considers both azimuth and elevation
domains for angle of arrival and angle of departure,
providing a more accurate representation of the
spatial characteristics of mmWave channels.