5g Beamforming

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

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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 2 © 2016 Dr.MBI@UKM 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 14

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5G Enabler Heterogeneous Network (HetNet)

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5G Enabler

Software defined control framework for heterogeneous RAN 16

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5G Enabler

Network slicing in software defined mobile networks 17

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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 22

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Beamforming Two adjacent cells each communicating with a respective UE located at the boundary between the two cells (eNB1UE1, eNB2UE2) 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. 23

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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 25

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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 user’s 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 30

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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 34

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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 what’s more, these performances can be achieved without obvious cost increase in wireless communication systems. 35

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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 37

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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 38

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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 41

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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. 43

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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 48

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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 56

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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 58

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Related Research  Estimating

DoA From Radio Frequency RSSI Measurements Using Multi-Element Femtocell Configuration

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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 60

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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. 61

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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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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 67

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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=??????

M

MAX_SINR

CAPACITY(M)=N * B.W * log1(1+MAX_SINR)

CAPACITY_AVG=N * (B.W/NO_USER ) * log1(1+MAX_SINR) 69

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Related Research

• Capacity Evaluation for UWB/mmWave Deployment in 5G System

Empirical CDF 1

3000 21 22

1000

0

-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

0

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

0

5

10 15 SINR (dB)

70

20

25

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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

1.66×10-1

1.59×10-1

1.28×10-1

0.0322

ECGSA

1.55×10-3

1.35×10-3

1.22×10-4

0.0011

SLGSA

16.04

10.80

7.09

10.12

HGSA

3.6×10-4

3.12×10-4

MBGSA

3.07×10-9

3.05×10-9

2.36×10-9

5.16×10-10

ECGSA

2.93×10-9

2.97×10-9

1.03×10-9

1.12×10-9

SLGSA

1.11×10-9

1.12×10-9

8.52×10-10

1.09×10-10

HGSA

8.81×10-10

7.84×10-10

1.23×10-10

5.63×10-10

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

2.48×10-2

1.48×10-2

0.00×100

0.027

SLGSA

0.03

2.19×10-2

0.00×100

0.030

HGSA

2.08×10-12

2.94×10-14

2.55×10-15

7.96×10-12

MBGSA

6.1×10-3

8.4×10-20

4.52×10-20

0.025

ECGSA

1.02×10-22

8.68×10-23

2.82×10-23

7.14×10-23

SLGSA

5.69×10-19

5.72×10-19

2.72×10-19

1.65×10-19

HGSA

2.65×10-23

2.22×10-23

1.08×10-23

1.4×10-23

3.65×10-5

0.0003

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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. 74

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um be r

(a)

cto rN

Sector Number 1

(b)

βCC2 = 900

2

Se

um be r cto rN Se

βCC1 = 1800 βCC2 = 1800

Sector Number 3

Sector Number 3

βCC1 is the Beam Angle of CC 1

βCC1 = 300

βCC2 is the Beam Angle of CC 2

βCC1 = 1500 Sector Number 1

βCC2 = 220

βCC1 = 450 βCC2 = 450

βCC1 = 3000 βCC2 = 3000

βCC1 = 3000 βCC2 = 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

βCC1 = 1800 βCC2 = 1800

cto rN

•

βCC1 = 450 βCC2 = 450

Se

Techniques Over Coordinated Contiguous Carrier Aggregation Deployment Scenario In LTEAdvanced System

2

• Efficient Adaptive Handover

2

Related Research

βCC2 = 3300

0

Coverage and Beam Pattern of CC1 Coverage and Beam Pattern of CC1

βCC1 = 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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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]

r

CDF Probability of User’s RSRP [P > Q

rxlevmin

]

1

0.6 0.5 0.4

4 3 2 1

0.3

CADS-1 CADS-2 CADS-3 CC-CADS

0

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]

1

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

0

2.4

2.6 2.8 3 3.2 3.4 3.6 3.8 Average UE’s Spectral Efficiency [bps/Hz]

4

4.2

CADS-1

CADS-2 CADS-3 CC-CADS Carrier Aggregation Deployment Scenarios

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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

6

2

0

1

5

-0.5

3

4 -1

-1.5 -1.5

-1

-0.5

0 km

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0.5

1

1.5

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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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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 CPqD’s 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 81

© 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 [email protected] [email protected] 019-2615404/019-3275425 03-89216326

UKM

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© 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)

86

© 2016 Dr.MBI@UKM

 Wireless

& Network

 Antenna

& Radio Frequency

 Photonics

 Space

Research

& Optical Communications

Science & Communications

87

© 2016 Dr.MBI@UKM

Research

88

© 2016 Dr.MBI@UKM