The 2nd International
Towards 5G System:
Conference on Wireless and
Issues
and
Telematics (ICWT 2016)
1-2 August 2016
Grand Aston Hotel Yogyakarta Indonesia
Challenges in
Beamforming
Prof. Dr. Mahamod Ismail
2016 Dr.MBI@UKM
Abstract
In order to meet Fifth Generation (5G) wireless system requirement in term
of user and system capacity, various disruptive technologies have been
proposed among other heterogeneous network (HetNets) over multiple
Radio Access Technologies (multi-RATs), Millimeter-wave, Massive MIMO
and Device-to-Device and Full-duplex communications. As 5G is
anticipated to operate in higher frequency, the propagation is more
hostile, however more elements can be packed into smaller antenna, thus
it become possible to steer the transmission towards the intended
direction and users using Direction-of-Arrival (DoA) information.
Traditionally, a beamforming is a signal processing techniques used to
control the directionality of the transmission and reception of radio signals,
thus the beam can be directed toward users and suppressed towards
interferers. Moreover, in 2G and 3G system, it been deployed using either
switched beam or adaptive beamformers in 2G and 3G system. Besides
several benefits in term of decreased interference, reduces overall
transmission power in networks, extended service and higher data rates in
sparse deployment, various issues and challenges need to be resolved for
5G beamforming deployment such as digital beamforming, DOA
estimations, Millimiter-wave beamforming and Massive MIMO
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beamforming.
Outline
Introduction
5G Enabler
Beamforming
BF Challenges
Related Research
Conclusion
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Introduction
Source: Qualcomm 2013
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Introduction
Source: Rumney 2014
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Introduction
Source: Roberts 2015
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3GPP Release-12 Onwards
MTC Machine-Type Communications
eMBMS - Evolved Multimedia Broadcast/Multicast Service
D2D Device-to-Device
Introduction
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3GPP Release-10
Source: Nagata 2014
Introduction
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3GPP Release-10
Source: Nagata 2014
Introduction
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3GPP Release-11
Source: Nagata 2014
Introduction
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5G Enabler
Source: Tafazolli 2015
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5G Enabler
High Capacity
High Throughput
High QoE
Efficiency
Latency < 1 ms
High Quality
User throughput ~ 1
Gbps
Low Latency
Avoid capacity crunch
with vast number of IoT
devices
Cost efficient high
density small cell
capacity and energy
efficient
Long Battery Life
Energy efficiency (up to
10 years)
Source: Roberts 2015 & Benn 2014
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5G Enabler
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5G Enabler
Heterogeneous Networks
Small cell, new carrier type, multiple RAT, D2D
Software Defined Cellular Networks
Massive MIMO and 3D MIMO
Machine to Machine Communications
Other Technologies
mmWave, shared spectrum, big data, indoor positioning
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5G Enabler
Heterogeneous Network (HetNet)
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5G Enabler
Software defined
control framework
for heterogeneous
RAN
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5G Enabler
Network slicing in software defined mobile networks
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5G Enabler
The features and
benefits of Release
12 work items
Massive MIMO
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5G Enabler
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5G Enabler
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5G Enabler
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Beamforming
Essentially
narrows a signal toward a receiver
Identified as a part of the solution to the 5G deployment
problem.
Already, beamforming is becoming a standard element
in many wireless scenarios, from Wi-Fi deployments to LTE
rollouts.
Benefit in Massive MIMO
Enhanced energy efficiency
Improved spectral efficiency
Enhanced data rate through
gain improvement
Increased system security
Improved link reliability
Applicable for mm wavebands
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Beamforming
Two adjacent cells each communicating with
a respective UE located at the boundary
between the two cells (eNB1UE1,
eNB2UE2) with maximum signal power in
the azimuth direction of serviced UE and by
steering the power null location in the
direction of interfered UE. Beamforming can
provide considerable performance
improvements particularly for cell edge users.
The beamforming gain can also be used to
increase the cell coverage where required.
A single cell (eNB3) communicating
simultaneously with two spatially separated
devices (UE3 and UE4). Since different
beamforming weightings can be applied
independently to each of the spatial
multiplexing transmission layers, it is possible
to use Space Division Multiple Access
(SDMA) in combination with MU-MIMO
transmissions in order to deliver an improved
cell capacity.
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Beamforming
Beamforming
Buttler Matrix
Switched
Beamforming
Adaptive
Beamforming
Non Blind Adaptive
Algorithms
Analog
Beamforming
Blind Adaptive
Algorithms
Digital
Beamforming
Hybrid
Beamforming
LMS
CMA
RLS
LS-CMA
Battler Matrix
SMI
LCMV
CGA
MVDR
Beamforming
classifications
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Beamforming
Switched beamforming vs adaptive beamforming
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Beamforming
SWITCHED BEAMFORMING
ADAPTIVE BEAMFORMING
COVERAGE AND
BETTER COVERAGE AND
WITH THE SAME POWER LEVEL,
CAPACITY
CAPACITY COMPARED TO
CONVENTIONAL ANTENNA
SYSTEMS. THE IMPROVEMENT IS
FROM 20 TO 200%.
SUFFERS FROM A PROBLEM IN
DIFFERENTIATING BETWEEN THE
DESIRED SIGNAL AND AN
INTERFERER SIGNAL
- EASY TO IMPLEMENT IN
EXISTING CELLULAR SYSTEMS
AND INEXPENSIVE.
- SIMPLE ALGORITHMS ARE
USED FOR BEAM SELECTION
CAN COVER A LARGER AND
UNIFORM AREA COMPARED TO
SWITCHED BEAMFORMING.
INTERFERENCE
ELIMINATION
COMPLEXITY AND
COST
OFFERS MORE COMPREHENSIVE
INTERFERENCE REJECTION
- VERY DIFFICULT TO
IMPLEMENT AND EXPENSIVE.
- REQUIRES TIME AND
ACCURATE ALGORITHMS
(VERY COMPLICATED) TO
STEER THE BEAM AND NULLS.
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Beamforming
Beamforming
utilizes multiple antennas
transmitting at the same frequency to realize
directional transmission
Open loop beamforming
Used
precomputed beamforming weights without
knowledge of the users location
Closed
loop beamforming
Employs
channel state information (CSI) to
calculate the beamweights
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Beamforming
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Beamforming
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Beamforming
Electrical downtilt
3D dynamic beamforming in
horizontal sight
Conventional 2D MIMO beamforming
3D dynamic beamforming in
vertical sight
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Beamforming
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Beamforming
Classification of Beamforming Techniques :
Direction of Arrival (DOA) beamforming
The eNodeB estimates the direction of arrival of the signal, uses
the DOA information to calculate the transmit weight, and targets
the major lobe of the transmit beam at the best direction.
MIMO beamforming:
The eNodeB uses the channel information to calculate the
transmit weight, forming a beam.
In the industry
TDD system uses open loop beamforming and
FDD system uses closed loop beamforming.
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Beamforming
Several AAS beamforming and beam steering
applications are possible for macro cell sites
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Beamforming
Applications of full-dimension MIMO (FD-MIMO) with 3D BF
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Beamforming
Multi-antenna technology is a wireless communication
technology which uses more than one antennas in both Base
Station (BS) and Mobile Station (MS) in many wireless
communication standards, such as 16e,16m,LTE,LTE-A
The technology brings:
Power Gain
Space Diversity Gain
Spatial Multiplexing Gain
Array Gain and
Co-channel Interference Reduction Gain.
Therefore, it is used to improve the system coverage,
enhance the link reliability and increase system capacity, and
whats more, these performances can be achieved without
obvious cost increase in wireless communication systems.
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Beamforming
Space-Time Block Coding (STBC)
achieve the Spatial Diversity Gain
offers redundancy in the spatial dimensions by transmitting a signal
on more than one antenna during two time slot.
Space Multiplexing (SM)
is for the Multiplexing Gain in MIMO system
it sends a different signal on each time-frequency resources of each
antenna
could multiply spectrum efficiency without additional spectrum
resources.
MIMO system
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Beamforming
Beamforming (BF) provide Array Gain and Co-channel Interference
Reduction Gain
By weighting the signal streams, the BS forms a narrow wave beams
which points to the direction of aim user while suppress the
interference signal from non-aim user.
Traditional BF technology is based on estimating the Direction of
Arrival (DOA) of beamforming phased-array and calculating the
beamforming weights based on channel coefficient matrix
The BF technology is also called MIMO-BF or MIMO BF.
Different with MIMO+BF, MIMO-BF or MIMO BF is solely BF without
being combined with MIMO Matrix A or MIMO Matrix B.
BF systems
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Beamforming
MIMO+BF Scheme 1 - based on the antenna sub-array & data transmission
MIMO+BF Scheme 2- based on the entire antenna array & data transmission
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Beamforming
The evolutionary path where Generation II moves the radio units from the indoor enclosure at
the base of a tower, up to the tower top below the antenna. RRU replaces coaxial feeder
cables with fiber-optic cable interconnects. Generation III integrates the radio unit, typically
2T4R, and antenna within the radome where the radio interfaces with a cross-polarized
antenna array. Generation IV integrates multiple radio transceivers inside the antenna where
each radio interfaces with a dedicated antenna element to form an array.
BTS Base Transceiver Station
RRU Remote Radio Unit
IAR Integrated Antenna Radio
AAS Active Antenna System
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Beamforming
Baseband Beamforming architectures
Provide large antenna gain and this enables multi stream, multi user
connections with a variety of transmission modes.
When the design requires hundreds of antennas, which all need hundreds
of power-hungry converters (both ADC and DAC) - increase hardware
complexity and power consumption of the system and makes this
architecture impractical for these types of designs.
Weighting factor Wi is a function of amplitude and phase with i {1..n} as
number of antenna paths, precoding and combining are performed in BB.
Baseband Beamforming architectures
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Beamforming
RF Beamforming architectures
The precoding and combining is done in the RF side with lower power
consumption and lower hardware complexity.
Since high performance phase shifters in CMOS introduce phase and
amplitude error verses frequency as well as phase variation verses the
control voltage, the design of high performance phase shifters in CMOS
turns out to be quite challenging.
Weighting factor Wi is a function of amplitude and phase with i {1..n} as
number of antenna paths, precoding and combining are performed in RF.
RF Beamforming
architectures
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Beamforming
Hybrid Beamforming architectures
The precoding and combining is done in both baseband (BB) and RF
sections. Baseband precoder(FBB) / combiner(WBB) using digital signal
processing and RF precoder (FRF) / combiner(WRF) using phase shifter.
By reducing the total number of the RF chains and ADC/DAC, hybrid
beamforming still gets similar performance to that of digital beamforming,
but saves power and complexity.
With this structure even though we used a large enough number of
antennas, the lossy mmWave channel naturally suppresses multi path
interference and reflections.
Hybrid Beamforming architectures
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Beamforming
Hybrid Precoding in mmWave and massive MIMO Systems
Designing hybrid analog/digital precoders/combiners is challenging mainly
because of the coupling between the analog and digital precoders.
Investigation on the hybrid precoding/combining design problem for singleuser/multi-user mmWave and low-frequency massive MIMO systems. Also hybrid
precoders design for wideband frequency selective mmWave systems.
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Beamforming
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3D
beamforming
Beamforming
Both vertical and horizontal directions
Vertical cell splitting (sectorization)
Beamforming
BF Challenges
FD-MIMO 3D
Beamforming
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BF Challenges
Rohde & Schwarz 2016
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BF Challenges
mmWave Beamforming
To provide high throughput in small geographic areas
Directional BF for signal power and reduced interference
Sensitivity to blockages, indoor coverage more challenging
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BF Challenges
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BF Challenges
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BF Challenges
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BF Challenges
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BF Challenges
Feedback for channel state information for hybrid
beamforming in 802.11ay
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BF Challenges
Efficient beam selection for hybrid beamforming
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Related Research
5G
Initiative in Malaysia
Was established on 3rd Sep 2014 initiated by Wireless
Communication Centre (WCC), Universiti Teknologi Malaysia
(UTM)
Members from universities, research institutions, industries and
Malaysian Technical Standards Forum Bhd. (MTSB)
MTSB is designated by Malaysian Communications and
Multimedia Commission (MCMC) and was established to
embrace self regulatory by initiating and facilitating the
development of technical codes, standards and guidelines
The objectives of 5G committee
To foster collaboration and partnership
between academia and industry in
5G R&D activities in Malaysia.
To contribute to the standardization
of IMT-2020
To become evaluation group for
IMT-2020 standardization
Source: Rahman, T.A. 2015
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Related Research
5G
Initiative in Malaysia
Source: Rahman, T.A. 2015
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Related Research
Pilot Contamination and its
Effect Towards Massive-MIMO
Capacity in Fifth Generation
(5G) Wireless Transmissions
Problem statements:
Pilot contamination is caused by the interference from all
users in the other cells during training phase
The effect of pilot contamination becomes worst when
all the nearby cells are time-synchronized cells
Pilot contamination caused asymptotic Signal to
Interference and Noise Ratio (SINR)
Objectives
To analyze the effect of pilot contamination that limit the
implementation of large number of Massive-MIMO
antenna
To investigate the relationship between spatial subchannel coefficients and channel estimation error under
5G downlink transmission requirements
To validate the performance of temporal-based pilot
contamination avoidance technique in higher order
Massive-MIMO
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Related Research
Estimating
DoA From Radio Frequency RSSI Measurements
Using Multi-Element Femtocell Configuration
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2015 Dr.MBI@UKM
Related Research
Interference Mitigation Strategies for Co-Existence
Among 5G Heterogeneous Networks
Sub-group
Work Package
DoA
Estimation
for 5G
femtocell
Interference
&
Coexistence
in 5G
5G Radio
Environment
al Map
D2D
interference
mitigation
Contributions
Improved beam steering based on
machine-learning algorithm
Localization issues related to 5G
femtocell deployment
Interference characterization in 5G
HetNet
Interference coordination
technique
Cross & co-layer interference in
D2D transmissions
Network offloading capabilities in
dense scenario
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Related Research
Problem Statement
Provision of directional beam
forming in femtocell
mandated by coverage
optimization and cell
mitigation
Future 5G wireless networks will
have to contend with severely
limited range at the high
frequencies at which they will
operate
Expect to see a proliferation of
5G base stations, including
multiple ones within a single
building.
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Related Research
Problem Statement
A handset usually communicates
though the nearest tower but
can be made to use a more
distant one if the nearest tower
cannot handle its traffic.
No evidence investigating Radio
Environment Map (REM) in
mitigating the intercell
interference.
What is not yet known is the role
of REM in facilitating small and
dense cells deployment in future
5G.
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2016 Dr.MBI@UKM
Related Research
Problem Statement
Device-to-Device (D2D) architecture improve
throughput, coverage, end-to-end latency.
However, introduces several challenges, such as
interference management between cellular and
D2D users becomes one of the most critical issues
for in-band D2D communication.
If the generated interference is not well
controlled, it will deteriorate the potential benefits
of D2D communication since the overall cellular
capacity and efficiency is degraded
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Related Research
Objectives
To
introduce a novel DoA estimation
technique of the users in 5G femtocell
network by using machine learning process
To quantify the benefits of REM-data
measurements experimentally in the intercell
interference coordination within 5G small cells
To design an innovative interference
cancellation technique to mitigate cross-layer
and co-layer interference in D2D enabled
cellular network.
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Related Research
Methodology
WP1: DoA Estimation for 5G Femtocell Multi-element Antenna
PHASE 1: Problem background and DoA characterization
PHASE 2: Development of beam steering technique based on
machine learning DoA algorithm
PHASE 3: Validation of beam steering in potential 5G
environment
WP2: Interference Mitigation for 5G Small Cells with Radio
Environment Map (REM) PHASE 1: Development of Spectrum
Sensing and Localisation Tracking
PHASE 2: Development of REM database
PHASE 3: Development of Intercell Interference Coordination
technique
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Related Research
WP 2: Overview of REM Prototype Architecture
REM Manager
Spatial interpolation toolbox
Propagation models toolbox
Statistical toolbox
REM Storage and
Acquisition unit
(REM SA)
...
Spectrum measurement data
REM Users
regulator
authorities
RRMs
MCDs information
Policy
Managers
network
admins
Transmitters/receivers
information
Propagation models
Radio Interference Fields
Statistical data
Measurement Capable
Devices (MCDs)
...
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Related Research
Methodology
WP3:
Cross & Co-Layer Interference Mitigation
Strategy for Device-to-Device (D2D)
PHASE 1: Investigation of interference cancellation
techniques in D2D enabled cellular networks and
5G transmission
PHASE 2: Exploring the feasibility of integrating
interference cancellation and Beamforming
precoding to D2D enabled cellular network
PHASE 3: Evaluate the interference cancellation
based on 5G specifications and network
offloading scenario
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Related Research
WP 3: Cellular Offloading in D2D Communications
in Multi-tier cells in Heterogeneous Networks
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Related Research
Capacity Evaluation for UWB/mmWave Deployment in
5G System
28 GHz
SINR A=??????
38 GHz
SINR B=??????
73 GHz
SINR C=??????
MAX_SINR
CAPACITY(M)=N * B.W * log1(1+MAX_SINR)
CAPACITY_AVG=N * (B.W/NO_USER ) * log1(1+MAX_SINR)
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Related Research
Capacity Evaluation for UWB/mmWave Deployment in
5G System
Empirical CDF
1
3000
21
22
1000
-1000
-2000
-3000
-3000
20
813
57 23 8210 3 717 19
35
50
27
66
1
29
15
36
34
49
28
26
11
12
36
43
9 8 87 24
3 65
2
265
63 84
12
48
47
25
37 10 4 647 5933
29 421 27
219 1 25
10
20
95 9431
769
86
11 12
281172 7 33
4690552 6 75
62
3
2
80
38
40 514596
32
3 356070 30
34
56 12 411
39 93
40
74
31
23
55
1
1
8
99
22
32
37 18
9168
6 92
54
4429 9798
1781 69
16
30
28
36
38 77
5 30
6 58 3867
4
21 20
13 78
15 14 15 14
42
39
100 41
1489
53 7 13 35
31 22 5 19 27
39 16
43
8
79
37
44
42 2617 18
88
23 24
1583 14 25 40
32
34
42
73
61
33
41
45 16 6 13 41
85
2
9
24
171018
46
48
47
9
-2000
-1000
1000
2000
0.8
0.7
P(SINR>absisca)
2000
SINR A
SINR B
SINR C
Max SINR
0.9
0.6
0.5
0.4
0.3
0.2
0.1
3000
0
-10
-5
10
15
SINR (dB)
70
20
25
30
2016 Dr.MBI@UKM
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Related Research
A Hybrid Gravitational
Search Algorithm (GSA)
for Enhancement of
Minimum Variance
Distortion-less Response
(MVDR) Beamforming
To develop and investigate the
MVDR beamforming algorithm
assisted by GSA so as to obtain a
deeper
null
at
interference
sources and more accurate
steering of main lobe toward
desired signal.
To analyses the performance of
the GSA so as to enable Hybrid
GSA (HGSA) based beamforming
algorithm to obtain its optimized
weight
vectors
with
better
throughput.
WMVDR
R 1a( )
H
a ( ) R 1a( )
W MVDR
71
W1
W
2
W M
2016 Dr.MBI@UKM
Related Research
Minimization result of benchmark functions with tmax=1000
Function
F1
F2
F3
F4
F5
Method
Mean
Median
Best
Std
MBGSA
1.6610-1
1.5910-1
1.2810-1
0.0322
ECGSA
1.5510-3
1.3510-3
1.2210-4
0.0011
SLGSA
16.04
10.80
7.09
10.12
HGSA
3.610-4
3.1210-4
MBGSA
3.0710-9
3.0510-9
2.3610-9
5.1610-10
ECGSA
2.9310-9
2.9710-9
1.0310-9
1.1210-9
SLGSA
1.1110-9
1.1210-9
8.5210-10
1.0910-10
HGSA
8.8110-10
7.8410-10
1.2310-10
5.6310-10
MBGSA
23.82
23.84
23.47
0.31
ECGSA
22.6
22.6
22.1
0.169
SLGSA
25.05
25.12
23.86
0.260
HGSA
21.94
22.19
20.13
0.79
MBGSA
1.28
1.38
0.07
0.34
ECGSA
2.4810-2
1.4810-2
0.00100
0.027
SLGSA
0.03
2.1910-2
0.00100
0.030
HGSA
2.0810-12
2.9410-14
2.5510-15
7.9610-12
MBGSA
6.110-3
8.410-20
4.5210-20
0.025
ECGSA
1.0210-22
8.6810-23
2.8210-23
7.1410-23
SLGSA
5.6910-19
5.7210-19
2.7210-19
1.6510-19
HGSA
2.6510-23
2.2210-23
1.0810-23
1.410-23
3.6510-5
0.0003
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2016 Dr.MBI@UKM
Related Research
Comparison
of SINR calculation for various cases
Method
1 Interference at
30
2 Interference at
30,50
3 Interference at
30,50,25
4 Interference at
30,50,25,60
MVDR
40.65
33.88
27.02
12.17
GSA-MVDR
67.10
63.65
32.25
12.52
MBGSA-MVDR
69.99
69.99
36.13
12.79
ECGSA-MVDR
69.99
69.99
36.61
12.79
SLGSA-MVDR
69.99
69.74
35.69
12.76
HGSA-MVDR
69.99
69.99
37.72
12.81
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Related Research
MVDR assisted by GSA perform better in terms of SINR in all
simulated scenarios as compared to conventional MVDR.
Three new modifications of GSA have been proposed as HGSA:
Memory Based Gravitational Search Algorithm (MBGSA)
Experience oriented-Convergence improved Gravitational Search
Algorithm (ECGSA)
Stochastic Leader Gravitational Search Algorithm (SL-GSA)
The HGSA-MVDR performs the best as compared to
conventional MVDR beamforming technique, GSA-MVDR,
MBGSA-MVDR, ECGSA-MVDR, SLGSA-MVDR beamforming
technique. HGSA-MVDR with high convergence rate is able to
determine the best weight vectors to produce better SINR in all
scenarios.
The HGSA performs the best as compared to conventional GSA
and its variants. HGSA with high convergence rate is able to
produce the best value in the benchmark functions.
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2016 Dr.MBI@UKM
um
be
r
(a)
cto
rN
Sector Number 1
(b)
CC2 = 900
Se
um
be
r
cto
rN
Se
CC1 = 1800
CC2 = 1800
Sector Number 3
Sector Number 3
CC1 is the Beam Angle of CC 1
CC1 = 300
CC2 is the Beam Angle of CC 2
CC1 = 1500
Sector Number 1
CC2 = 220
CC1 = 450
CC2 = 450
CC1 = 3000
CC2 = 3000
CC1 = 3000
CC2 = 3000
um
be
r
CC-CADS deployment scheme is using
two contiguous CCs with different beam
orientation for each carrier to enhance
the coverage of the eNB
Sector Number 1
CC1 = 1800
CC2 = 1800
cto
rN
CC1 = 450
CC2 = 450
Se
Techniques Over Coordinated
Contiguous Carrier Aggregation
Deployment Scenario In LTEAdvanced System
Efficient Adaptive Handover
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CC2 = 3300
Coverage and Beam Pattern
of CC1
Coverage and Beam Pattern
of CC1
CC1 = 2700
Sector Number 3
(c)
(a) CADS-1, (b) CADS-2, and (c) CADS-3
eNB2
eNB1
eNB3
eNB4
CC1 (F1)
Sector - 1
CC2 (F2)
Sector - 2
Sector - 3
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2016 Dr.MBI@UKM
Related Research
The average RSRP, SINR, spectral efficiency and outage probability in CCCADS scenario are significantly better compared to the typical CADSs.
Empirical CDF
8
7
CADS-1
CADS-2
CADS-3
CC-CADS
0.9
0.8
6
5
0.7
Average SINR [dB]
CDF Probability of Users RSRP [P > Q
rxlevmin
0.6
0.5
0.4
4
3
2
1
0.3
CADS-1
CADS-2
CADS-3
CC-CADS
0.2
-1
0.1
0
-57
-2
-54.5
-56
-55
-54
-53
-52
-51
Average Serving RSRP [Pr (dBm)]
-50
-49
-54
-53.5
-53
-52.5
-52
Average Serving RSRP [dBm]
Average Outage Probability [ < ]
thr
CADS-1
CADS-2
CADS-3
CC-CADS
0.8
0.7
0.6
0.5
0.4
0.3
-51
40km
60km
80km
100km
120km
140km
0.3
0.9
-51.5
0.35
Empirical CDF
CDF of Spectral Efficiency Probability
0.25
0.2
0.15
0.1
0.05
0.2
0.1
0
2.2
2.4
2.6
2.8
3
3.2
3.4
3.6
3.8
Average UEs Spectral Efficiency [bps/Hz]
4.2
CADS-1
CADS-2
CADS-3
CC-CADS
Carrier Aggregation Deployment Scenarios
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2016 Dr.MBI@UKM
Related Research
Steerable Beamforming
Techniques over Carrier
Aggregation in LTE-Advanced
System
Interference mitigation using antenna
beam steering coordinated with CarrierAggregation for capacity enhancement
1
7
0.5
km
Cell Layout
1.5
-0.5
4
-1
-1.5
-1.5
-1
-0.5
0
km
77
0.5
1.5
2016 Dr.MBI@UKM
Related Research
SINR for F1 (2.1GHz)
1
0.9
0.8
F1 for 10 UE
F1 for 50 UE
F1 for 100 UE
0.7
X: 6.484
Y: 0.5
0.5
0.4
SINR for F2 (2.6GHz)
1
0.3
0.2
0.9
0.1
0.8
0
-60
F2 for 10 UE
F2 for 50 UE
F2 for 100 UE
0.7
-40
-20
0
20
SINR (dB)
40
60
80
0.6
F(x)
F(x)
0.6
SINR performance
X: 20.53
Y: 0.5
0.5
0.4
0.3
0.2
0.1
0
-60
-40
-20
0
20
SINR (dB)
78
40
60
80
2016 Dr.MBI@UKM
Related Research
Current
Grants
A New DoA Estimation Technique based on Multi-element
Antenna configuration in Femtocell for 5G Cellular Mobile
Communication
Autonomous Multi-objective Cross-layer Optimization for
Ultra-dense 5G Cellular Networks
Pilot Contamination and its Effect Towards Massive-MIMO
Capacity in Fifth Generation (5G) Wireless Transmissions
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2016 Dr.MBI@UKM
Conclusion
The
promising 5G technology is totally a new
technology that utilizes multiple Radio Access
Technologies (RAT) to meet users demand.
Among others, interference mitigation and capacity
enhancement are two important issues to be resolved
before 5G deployment.
Massive MIMO and 3D beamforming is one of the
potential solution for spectral efficiency enhancement.
However, there are many challenges to be resolve
before system deployment at mmWave frequencies
(30 GHz and 60 GHz)
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2016 Dr.MBI@UKM
References
Wonil Roh. 2015. Advanced MIMO/Beamforming as Key Enabler for
5G. Johannesberg Summit. May 2016.
Chin, Woon Hau, Zhong Fan, and Russell J. Haines. "Emerging
Technologies and Research Challenges for 5G Wireless Networks."
IEEE Wireless Communications April 2014.
Akhil Gupta & Rakesh Kumar Jha. A Survey of 5G Network:
Architecture and Emerging Technologies. IEEE Access. 2015
Miranda, J.P. 2014. Interference Mitigation & Massive MIMO for 5G:
Summary of CPqDs Results.
Shayea, I., M. Ismail, R. Nordin & H. Mohamad 2014. Handover
Performance over a Coordinated Contiguous Carrier Aggregation
Deployment Scenario in the LTE-Advanced System. International
Journal of Vehicular Technology 2014(15):1-15.
Tharek Abd. Rahman. 2015. Malaysian Towards 5G: Standardization
and R&D Activities. 5G IMT Seminar
Rahim Tafazolli. 2015. 5G: Special Generation. 5G IMT Seminar
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2016 Dr.MBI@UKM
References
Konstantinos Dimou. 2013. Interference Management Within 3GPP
LTE-Advanced.
Phil Roberts, 5G is this the technology that will deliver the ultimate
mobile experience? 2015 (http://telecom.com)
Qian Li,Huaning Niu, Apostolos Papathanassiou & Geng Wu. 5G
Network Capacity. IEEE Vehicular Technology Magazine. March 2014
Moray Rumney. Keysight Technologies - Finding Space for 5G. 2014
Howard Benn, Vision and Key Features for 5th Generation (5G)
Cellular. 2014
Afaz Uddin Ahmed, Mohammad Tariqul Islam, and Mahamod Ismail.
2015. Estimating DoA From Radio Frequency RSSI Measurements Using
Multi-Element Femtocell Configuration. IEEE Sensors Journal
15(4):2087-2092.
http://www.telecomclouds.org/wp-content/uploads/2013/11/. 2015
Zahir, T., Arshad, K., Nakata, A., and Moessner, K. Moessner, K.,
Interference Management in Femtocells, IEEE Communications
Surveys & Tutorials, 15(1):293-311. 2013.
82
2016 Dr.MBI@UKM
Thank you
http://www.ukm.my/mahamod
mahamod@ukm.edu.my
mahamod@gmail.com
019-2615404/019-3275425
03-89216326
UKM
84
2016 Dr.MBI@UKM
Department
http://www.ukm.my/jkees/
Academic
Staff: Professor (13), Associate Professor (9),
Senior Lecturer (25), Lecturer (8)
Supporting Staff: Technical (21), Administration (3)
Academic Program:
Bachelor of Engineering (Electrical and Electronics Engineering) 80
Bachelor of Engineering (Electronic Engineering) 60
M.Eng. (Communication & Computer) 40
85
2016 Dr.MBI@UKM
M.Sc. (Microelectronics) 20
Research
Research
1.
2.
3.
4.
Computer Technology, Signal Processing and
Instrumentation
Microelectronics, Optical fibers and Sensor Technology
Power and Expert Systems
Communications and Telematics
Research
1.
2.
Group:
Institute/Centre:
Institute of Microengineering and Nanoelectronics (IMEN)
Space Science Centre (ANGKASA)
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2016 Dr.MBI@UKM
Wireless
& Network
Antenna
& Radio Frequency
Photonics
Space
Research
& Optical Communications
Science & Communications
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2016 Dr.MBI@UKM
Research
88
2016 Dr.MBI@UKM
