PERFORMANCE ENHANCEMENT TECHNIQUES FOR LARGE-SCALE ANTENNA ARRAYS BASED COMMUNICATION AND RADAR SYSTEMS

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PERFORMANCE ENHANCEMENT TECHNIQUES FOR LARGE-SCALE

ANTENNA ARRAYS BASED COMMUNICATION AND RADAR

SYSTEMS

A THESIS SUBMITTED TO THE UNIVERSITY OF MANCHESTER FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN THE

FACULTY OF SCIENCE AND ENGINEERING

2020

By Murat Temiz

Department of Electrical and Electronic Engineering

School of Engineering

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List of Figures

2.1 An illustration of OFDM subcarriers in the frequency domain. . . 30 2.2 OFDM modulation and demodulation. . . 32 2.3 Simulation results of a patch antenna in CST microwave studio. . . 37 4.1 The designed single antenna element: (a) front, (b) side and (c) back views. . 57 4.2 (a) The measured and simulated S11 response of the single antenna and (b)

measured radiation pattern of the antenna in the anechoic chamber. . . 58 4.3 (a) Uniform and (b) shifted array geometries. . . 59 4.4 Front views of the fabricated single antenna and antenna arrays. The ground

plane of the antenna is presented in the top-right corner. . . 60 4.5 Radiation patterns of the arrays: (a) URA (b) SRA. . . 60 4.6 The gains of the single element and the antenna arrays in simulations. Blue

diamonds indicates the measured gain. . . 61 4.7 Measured mutual couplings in (a) URA and (b) SRA. . . 62 4.8 Measurement setup: The BS arrays and VNA are in the first office and UEs

are in the other office. Orange and the gray squares represent office desks and metal office cabinet. . . 63 4.9 Average power delay profiles of the signals received by (a) URA and (b) SRA

from the UEs. . . 65 4.10 Measured channel correlations (|φ|) among all UE channels with (a) URA

and (b) SRA. . . 67 4.11 Average intra-UE correlations of each UE and inter-UEs correlations among

UE1, UE3 and UE6. . . 68 4.12 Average received powers by the arrays from UEs. . . 69 4.13 CSI estimation errors with MMSE estimator. . . 73 4.14 Throughput per UE based on the measured channel data obtained with two

arrays. The solid lines indicate simulation results with 128-QAM-OFDM, and the dashed lines indicate the channel capacities. . . 74 4.15 SER with different arrays with and without PC. . . 75 5.1 The simplified block diagram of the quantized massive MIMO uplink through

a correlated H channel. . . 80 5.2 (a) Uniform linear vertical (1D) and (b) uniform rectangular (2D) antenna

arrangements. . . 84

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LIST OF FIGURES 7 5.3 Average BER as a function of SNR for 1D and 2D antenna arrangement with

different channel correlation (φ) coefficients. (Other parameters: M = 100 BS Antennas, K = 10 UEs, 64−QAM, 3−bit ADCs and perfect CSI). . . 89 5.4 The impact of the correlation on the mutual information of the uplink. (2−bit

ADCs, M = 100, K = 10 and SNR = 5 dB). . . 90 5.5 The joint impact of the channel correlation and the CSI mismatch on the

mutual information per user for; (a) 16−QAM uplink with 2−bit ADCs, (b) 64−QAM uplink with 3−bit ADCs (M = 100, K = 10, SNR = 10 dB and with the SRRC filter). . . 90 5.6 The joint impact of middle level channel correlation (φ = 0.6) and CSI es-

timation errors with 1−bit ADCs on the mutual information. (M = 100, K = 10) . . . 91 5.7 The joint impact of middle level channel correlation (φ = 0.6) and CSI es-

timation errors with 2−bit ADCs on the mutual information. ( M = 100, K = 10). . . 92 5.8 Combined effects of middle level channel correlation (φ = 0.6) and CSI es-

timation errors with 3−bit ADCs on the mutual information. (M = 100, K = 10). . . 92 5.9 (a) Spectral efficiency and (b) required energy consumption per bit data for

ADCs under a high channel correlation (φ = 0.8) and CSI estimation errors (α = 1.5 and (β = 15). (M = 100, K = 10). . . 94 6.1 A dual-functional BS which beamforms communication symbols towards the

downlink UEs while emitting an radar waveform into all directions. . . 99 6.2 A prototype downlink RadCom architecture with communication and radar

antennas on the left. The virtual antenna array given on the left-bottom is obtained by MIMO radar processing of two transmit antennas from the radar receive antennas. The uplink communication blocks are not shown here. . . 100 6.3 Synchronized RadCom TDD operation mode . . . 102 6.4 Arbitrary OFDM waveforms transmitted by two radar transmit antennas and

OFDM communication symbols transmitted by one communication antenna. 102 6.5 Sum-capacity of the network as a function of number of UEs under radar

interference with Ψ = 1 and Ψ = 3. K = 10, M = 100, SNR is ρ = 5 dB.

Channel estimation error parameters β = 0.3 and α = 0.8. . . 120 6.6 Capacity of RadCom system under various radar-communication power out-

put ratios and number of BS antennas, K = 10 and SNR is 5 dB. . . 121 6.7 Capacity per UE of RadCom systems with different methods. . . 121 6.8 Performance of different radar waveform modulation schemes (QPSK, 16-PSK

and 256-PSK). . . 122 6.9 Estimated range-velocity profiles of two targets; (a) with the direct coup-

ling, (b) after eliminating the direct coupling. And estimated range-angle (azimuth) profiles of the targets using the Fourier beamforming method; (c) without MIMO (d) with MIMO radar processing. P = 2, Q = 10.. . . 123

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LIST OF FIGURES 8 6.10 Communication sum-capacity and radar probability of detection (PD) under

various radar-communication power ratios (Ψ) with M = 50 and M = 100 BS array elements. SNR = 5 dB, K = 10 UEs, pF A= 10⁻⁶. . . 124 6.11 Computational complexity of the proposed schemes, M = 100 BS antennas. . 124 7.1 A prototype system model where the BS communicates with downlink UEs by

precoding data onto them and detects the targets in the range simultaneously. 131 7.2 SINR gain β²ZF obtained by ORWF over the RRWF with various number of

UEs (K) and radar-communication power ratio (Ψ) for M = 100 BS antennas.141 7.3 Detection of a single target using OFDM radar with four different radar SINR

values. . . 144 7.4 Sum-capacity of RadCom network with different precoding and power alloc-

ation schemes with regards to increasing radar-communication power ratio.

M = 100, pcom= 10W, SNR=pcom/σ_n². . . 149 7.5 Energy efficiency as a function of Ψ, M = 100, K = 10, P = 10 W,

SNR=pcom/σ_n². . . 150 7.6 Energy efficiency of the RadCom network with different precoding schemes.

M = 100, K = 10 . . . 151 7.7 Optimum energy efficiency of the RadCom network with different number of

UEs. M = 100 . . . 151 7.8 Optimized sum-capacity of the network with various number of BS and an-

tennas and UEs, ρ = 8 dB. . . 152 7.9 Optimized EE of the network with various number of BS and antennas and

UEs. ρ = 8 dB. . . 153 8.1 (a) Uplink massive MIMO communication with radar sensing, (b) Synchron-

ized TDD frame for communication and radar. . . 158 8.2 Block diagram of uplink massive MIMO OFDM RadCom. . . 162 8.3 Sum-capacity of the network as a function of communication SNR in the

uplink RadCom with perfect CSI i.e., ξ = 0. prad = pcom, M = 100 BS antennas, K = 10 UEs. . . 171 8.4 Sum-capacity of the network as a function of communication SNR in the

uplink RadCom with imperfect CSI i.e., ξ = 0.05. prad= pcom, M = 100 BS antennas, K = 10 UEs. . . 171 8.5 Impact of channel estimation errors on the network sum-capacity in the uplink

RadCom where SI is completely cancelled out with DSP canceller. prad = pcom, M = 100 BS antennas, K = 10 UEs. . . 172 8.6 Sum-capacity of uplink RadCom with various number of BS antennas. prad=

p_com, K = 10 UEs, and SNR is 10 dB. . . 172 8.7 Sum-capacity and radar channel estimation errors as communication SNR

increases within various network conditions. prad = p_com, M = 100 BS antennas, K = 10 UEs. . . 173

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LIST OF FIGURES 9 8.8 Sum-capacity and radar channel estimation errors as a function of radar power

output (prad). pcom = 1, M = 100 BS antennas, K = 10 UEs, and communication SNR is 10 dB. . . 174 8.9 Sum-capacity and radar image SINR after OFDM radar processing as a func-

tion of radar output power prad. pcom=1, M = 100 BS antennas, K = 10 UEs, and communication SNR is 10 dB. . . 174 8.10 Radar images of two targets with (a) SI and (b) after cancelling the SI com-

pletely. Radar SINR after radar processing is 12 dB. . . 175 8.11 Radar images of two targets with (a) SI and (b) after cancelling the SI com-

pletely. Radar SINR after radar processing is 17 dB. . . 175 8.12 Radar images of two targets with (a) SI and (b) after cancelling the SI com-

pletely. Radar SINR after radar processing is 22 dB. . . 176

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List of Tables

4.1 The details of the UE locations. . . 62

4.2 Massive MIMO System Parameters . . . 74

5.1 Required energy per conversion for ADCs with different resolutions which operate at 800 MSps rate. . . 93

6.1 Complexity of the equations used in Algorithm 1. . . 111

6.2 Parameters used in simulations. . . 118

7.1 Parameters of the RadCom system. . . 148

8.1 Uplink RadCom system parameters . . . 170

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List of abbreviations

MIMO Multiple-input-multiple-Output CSI Channel state information SNR Signal to noise ratio

SINR Signal to interference and noise ratio LOS Line-of-sight

NLOS Non-line-of-sight mmWave Millimeter save BS Base station UE User equipment RF Radio frequency EM Electromagnetic waves AOA Angle of arrival AOD Angle of departure SRA Shifted rectangular array ULA Uniform linear array URA Uniform rectangular array UCA Uniform cylindrical array ZF Zero-Forcing

DPC Dirty paper coding

ADC Analogue-to-digital converter DAC Digital-to-analogue converter QPSK Quadrature phase shift keying QAM Quadrature amplitude modulation BER Bit error rate

SER Symbol error rate MRC Maximal-ratio combining LS Least squares

MMSE Minimum-mean-square-error

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List of abbreviations

OFDM Orthogonal frequency-division multiplexing FDD Frequency-division duplex

TDD Time-division duplex ICI Inter-channel interference PQN Pseudo-quantization noise AQN Additive-quantization noise AWGN Additive white Gaussian noise LTE Long-term evolution

5G Fifth generation UWB Ultra wide band S11 Reflection Coefficient S21 Transmission Coefficient

TEM Transverse electromagnetic waves

CW Clockwise

CCW Counter clockwise H-field Magnetic field E-field Electric field

FPGA Field-programmable gate array SRRC Square-root-raised-cosine filter ISI Inter-symbol interference

SINQR Signal to interference noise, and quantization noise ratio MSps Mega sample per second

RadCom Radar communication AP Access point

Tx Transmitter

Rx Receiver

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List of Symbols

λ Wavelength

c0 The speed of light ε_r Relative permittivity Hcom or H Communication channel Hrad Radar channel

W Precoder matrix

βk Large-scale fading gain of the kth UE h_k Channel vector of the kth UE

fc Carrier freqeuncy

BW Bandwidth

βZF and αZF ZF scaling factors

Gtand Gr Transmitter and receiver antenna gains G_p OFDM radar processing gain

E Channel estimated error matrix Hˆ Estimated CSI matrix

ξ Error variance

σ²_n Noise variance

fD Doppler shift

v velocity

d₀ Reference distance dk Distance of the kth UE p_com Communication power output prad Radar power output

γSI Residual self-interference

M Number of BS antennas

K Number of UEs

τ Coherence time

∆t TDD frame duration

∆f OFDM subcarrier spacing Nc Number of subcarriers Nsym Number of symbols

C Mutual coupling matrix

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Mathematical Notations

P Summation

∀x For all values of x O Floating point operation E [] Expectation operator

|| Absolute value operator kk L₂-norm operator kk_F Frobenius norm operator ()^H Hermitian transpose ()^∗ Conjugate

()^T Transpose

argmax Arguments of the maxima diag() Diagonal matrix

I_n Identity matrix

H Matrix (Bold upper case letters) h Vector (Bold lower case letters) tr () Trace operator

CN () Complex-valued Gaussian distribution

< () Real part of a complex number

= () Imaginary part of a complex number Q_n(.) n−bit quantized signal

sign (.) Sign of a value

()_com Communication related parameter ()_rad Radar related parameter

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Abstract

D

uring the last decade, the number of connected devices to mobile communication networks and their data-rate demands have enormously increased. As a consequence, efficient and intelligent utilization of frequency resources is required to satisfy these demands for future communication networks due to the limited available frequency spectrum. Meanwhile, growing demand for radar sensing applications has been recently observed especially in vehicular networks where sensing and communications are two paramount technologies to empower safe, efficient, and smart vehicular systems. Radar sensing also necessitates wideband frequency resources to accurately estimate the target parameters such as range, velocity and angle. Massive multiple-input-multiple-output (MIMO) is the most promising solution to efficiently utilize available frequency spectrum since it enables the base station (BS) or access point (AP) to simultaneously communicate with multiple users (UEs) in the same time-frequency resources. Furthermore, dual-functional radar and communication (RadCom) platforms can also enhance the energy and spectral efficiencies by utilising the same hardware and frequency resources for radar sensing and communications.

Motivated by the aforementioned advantages of massive MIMO communication and Rad- Com systems, this thesis proposes techniques and algorithms to enhance the spectral and energy efficiencies of massive MIMO networks and enable joint radar sensing and communications. Firstly, we investigated the impact of the array geometry on the performance of massive MIMO networks and proposed an antenna array geometry which reduces the channel correlation among UEs and enhances the spectral efficiency of the network. The performance of the proposed array geometry is examined by simulations and measurements in a prototyped indoor massive MIMO network. Moreover, based on the measurement data, a practical power control algorithm for uplink massive MIMO networks is proposed. Secondly, we examined low-resolution analogue-to-digital converters (ADCs) in massive MIMO networks in order to improve the energy efficiency of the network and revealed the optimum modulation schemes for each ADC resolution. Furthermore, the trade-off between energy and spectral efficiencies with regards to ADC resolutions is investigated, and employing a root-raised-cosine filter is studied to mitigate the impact of the coarse quantization.

Thirdly, we introduced techniques and algorithms to enable joint massive MIMO communication and OFDM radar sensing by exploiting the interference between radar and communication subsystems. For joint downlink communications and radar sensing, a novel RadCom architecture with an interference utilization precoder is proposed, and its communication capacity and radar detection accuracy are mathematically analysed and verified

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CHAPTER 0. ABSTRACT 16 via simulations. This novel RadCom architecture is shown to provide a substantial capacity improvement while enabling simultaneous radar sensing. Furthermore, the proposed RadCom precoder has been optimized via convex optimization to improve the energy and spectral efficiencies of the network. The capacity of the optimized precoders are analysed and compared to the other techniques. Lastly, an uplink RadCom architecture, which utilizes successive interference cancellation (SIC) and self-interference (SI) cancellation techniques, is proposed and comprehensively analysed. Mathematical analysis and simulation results show that the proposed uplink RadCom architecture can efficiently sense the environment while communicating with multiple uplink UEs.

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Declaration

No portion of the work referred to in this thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning.

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Copyright

1. The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he has given The University of Manchester certain rights to use such Copyright, including for adminis- trative purposes.

2. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made.

3. The ownership of certain Copyright, patents, designs, trademarks and other intellectual property (the “Intellectual Property”) and any reproductions of copyright works in the thesis, for example graphs and tables (“Reproductions”), which may be described in this thesis, may not be owned by the author and may be owned by third parties.

Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions.

4. Further information on the conditions under which disclosure, publication and com- mercialisation of this thesis, the Copyright and any Intellectual Property and/or Re- productions described in it may take place is available in the University IP Policy (see http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=24420), in any relevant Thesis restriction declarations deposited in the University Library, The University Library’s regulations (see http://www.library.manchester.ac.uk/about/regulations/) and in The University’s policy on Presentation of Theses.

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Acknowledgements

I would like to express my sincere appreciation to my supervisor, Dr Emad Alsusa, for his guidance and inspiring me during my PhD research. His rich expertise, solid knowledge, enthusiasm and dedication to state-of-the-art research have remarkably contributed to my PhD and research career. I also appreciate Dr Laith Danoon, Dr David Zhang and Dr Mo- hammed Baidas for their cooperation and advice with their excellent technical expertise in their respective fields, fruitful discussions and continuously providing constructive feedback on my research.

I am also delighted to know many friends and colleagues in the microwave and communications research group, including Dr Moiz, Dr Priya, Dr Sarah, Dr Kenan, Mutasem, Yao, Han, Shookoh and others. I appreciate all the refreshing times and scientific discussions we had while working on our research. Furthermore, I sincerely thank the Turkish Ministry of National Education for their financial support during my PhD study. Finally, I would like to express my gratitude to my parents for their continuous support.

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Dedication

I dedicate this thesis to my father and mother, who always support me.

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Chapter 1 Introduction

1.1 Background and Motivation

T

he wireless communication technologies have become paramount parts of our lives in the last 20 years and their importance has been increasing with each passing day.

Not only the data traffic but also the number of connected devices to the networks has been rapidly growing as a result of the increasing usage of smart devices, sensing applications, mobile phones, tablets, computers, smartwatches and other wireless devices. Moreover, it is expected that this data-rate and connectivity demand will be substantially more in the future. To satisfy this enormous connectivity and data-rate demands, wireless communication networks have been evolving rapidly.

When we look at the brief history of cellular mobile networks from the first generation (1G) to the current generation (5G), it is clear that the network capacity and the number of connected devices have been increasing to satisfy the growing demand for wireless communication technologies with each passing day. 1G mobile networks were launched in the 1980s and they were solely for voice communication over narrow-band frequency channels using analogue technologies [1]. After that, the second generation (2G) mobile networks were introduced with significant improvements such as supporting digital voice communication, short message service (SMS) and general packet radio service (GPRS) to provide mobile internet connection. Furthermore, the later versions of 2G included enhanced data rates for GSM evolution (EDGE) as a technology to offer higher data-rates. In early 2000s, the third generation (3G) networks were standardized and started being deployed worldwide.

3G offered substantially improved data-rates compared to 2G (i.e., 64 kbps in 2G and 2 Mbps in 3G) in order to enable the first mobile broadband internet connection. Major improvements were seen with the introduction of 4G (long term evolution-advanced (LTE-A)) which offered high-speed downlink (up to 300 Mbps) and uplink (50 Mbps) connections by utilizing much wider bandwidths up to 100 MHz [1, 2]. Ultimately, the first version of the fifth-generation (5G) mobile network standards has been recently released by the 3rd Generation Partnership Project (3GPP) and European Telecommunications Standards In- stitute (ETSI) [3]. In addition to the substantially improved communication capacity, 5G networks are also designed to provide low-latency, ultra-reliable communication and massive

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CHAPTER 1. INTRODUCTION 22 connectivity for various applications.

In the 5G standards technical report [3], four major targeted applications of 5G New Radio (NR) are highlighted as:

• Enhanced Mobile Broadband (eMBB): These applications require greatly improved data-rates with a moderate latency even with massive connectivity and high- speed scenarios. For instance, the initial release of 5G NR standards supports 1024- QAM so as to reach up to 3.5 Gbps downlink data-rate for eMBB applications [4,5].

• Critical Communications (CC) and Ultra-Reliable and Low Latency Com- munications (URLLC):Safety or mission-critical applications, e.g., industrial automation, autonomous vehicles and virtual reality applications [4], require very low- latency and ultra-reliable (99.999%) communication infrastructure. Thus, 5G beyond networks should support very short end-to-end delay and very high reliability.

• Massive Internet of Things (mIoT): This application scenario requires the network to support a large number of connected devices and handle heavy mobile traffic loads. These IoT devices might require low-power and low data-rate connections to prolong their lifetime in the network since they might be powered by low-cost batter- ies [6].

• Flexible network operations: 5G mobile networks must also be flexible and scalable to adapt themselves to dynamically changing network environment and communication demands. This flexibility and scalability may be provided by network slicing, software- defined networking (SDN) and network function virtualization (NFV) technologies [7, 8].

Furthermore, 5G networks are also designed to support millimetre-wave (i.e., 24.25 - 52.6 GHz bands initially) carrier frequency with wideband channels for ultra-high data-rate communications in addition to sub-6 GHz frequencies [3,9]. The aforementioned major applications of 5G NR require the efficient and effective utilization of already congested and scarce radio frequency spectrum in addition to energy-efficient data transmission to reduce the power consumption of the networks while the number of connected devices grows. As a result, the physical layer of communication networks must efficiently utilize the limited frequency and energy resources. One of the most prominent solutions for this is the massive multi-user multiple-input-multiple-output (massive MIMO) technique which exploits the spatial diversity and antenna array gain in order to enable ultra-high data-rates and out- standingly reliable communication. In addition to this, massive MIMO makes mmWave communication feasible and efficient as it mitigates extremely high path loss encountered by mmWaves by employing large-scale antenna arrays to perform beamforming. For instance, this enables utilization of more than 1 GHz bandwidth at mmWaves for wireless backhaul [10], and ultra-high data-rate short-range communication in heterogeneous networks (HetNet) [11, 12]. Therefore, massive MIMO is one of the core enabling technologies for 5G and beyond mobile networks, and it has already taken place in the first 5G NR standards [3].

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CHAPTER 1. INTRODUCTION 23 Briefly looking at the history of multi-antenna communication, one could see that the serious attempts to analyse and understand multiple antenna systems for mobile communications were initiated by Foschini et al. and Teletar in their pioneering works [13, 14]. They considered that a few antennas are employed at the transmitter and receiver to communicate through multiple parallel data-streams, and revealed that this implementation significantly improves the channel capacity. This communication between one transmitter and one receiver via multiple antennas is known as single-user¹ multiple-input-multiple-output (SU-MIMO) communication and it requires the same number of antennas at both ends to effectively and efficiently operate. However, considering that UEs are usually small in size and battery-powered while the BS is fixed and much larger in mobile networks. Therefore, it is not possible to employ a large number of antennas at the UEs, which restricts the capabilities of SU-MIMO. However, considering multi-user communication, actually more antennas at the BS can be utilized to simultaneously communicate with more than one UEs using the same time-frequency resources [15–17], and this is known as multi-user MIMO (MU-MIMO) communication. In this case, the multiplexing gain is exploited to serve multiple UEs. Fur- thermore, employing a large number of antennas (e.g., > 64) at the BS is feasible to serve plenty of UEs (e.g., > 10) which are equipped with one or more antennas. This setup is known as massive MIMO or large-scale antenna systems (LSAS). In this case, UEs need only one antenna with low-cost hardware and do not require complex physical layer algorithms since the channel estimation, precoding/beamforming data during downlink and receiving data during uplink are performed by the BS. In addition to providing substantial capacity enhancement, massive MIMO also enables energy-efficient communication by focusing the energy onto the desired UEs and exploiting the antenna array gain [17, 18]. Consequently, the importance of massive MIMO has been increasing with each passing day. Further- more, massive MIMO and related technologies (e.g., cell-free massive MIMO, holographic MIMO, mmWave/THz MIMO and intelligent reflecting surfaces (IRS)) are also envisaged to constitute the core of 5G beyond and 6G mobile networks [19–21].

Although massive MIMO might offer unlimited capacity with an unlimited number of antennas in favourable channel conditions, its performance may be significantly degraded by some physical layer phenomenon or hardware impairments such as channel correlation, channel estimation errors, mutual coupling, transceiver impairments, pilot contamination, phase noise and synchronization errors [22–24]. Consequently, these issues should be considered while designing massive MIMO networks. The energy efficiency of communication networks is another important aspect that needs to be considered while improving their performance and quality of service. Although massive MIMO offers substantially enhanced transmit power efficiency by focusing the energy onto the desired UEs, hardware components may consume significantly high power to process high throughput data that is transmitted or received. For instance, in view of the fact that utilizing ultra-wideband channels at mmWave frequencies would also require ultra-high-speed analogue-to-digital converters (ADCs) (e.g., 10 Gsps sampling rate) and such ADCs tend to consume a substantial amount of power to maintain their operations since their power dissipation is directly related to sampling rate and resolution [25]. One solution to reduce the power consumption of ADCs is opting for 1-

1It is also called point-to-point MIMO in the literature.

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CHAPTER 1. INTRODUCTION 24 bit or low-resolution ADCs which have much simpler circuitry, hence consumes significantly less power compared to their high-resolution counterparts [26, 27]. It is worth noting that quantization errors caused by coarse quantization in the case of employing low-resolution ADCs are mitigated by having a large number of antennas as the multiple copies of the transmitted signals are received through different channels.

On the other hand, radio sensing technologies have also commenced taking place in commercial and industrial applications in addition to military applications. For instance, autonomous driving requires extensive knowledge of the surrounding of vehicles to provide safe and intelligent driving [28–30]. There are also other possible applications of radar sensing such as detection of drones [31–33] and industrial or factory automation [34]. These applications also require reliable communication to safely and seamlessly operate, and hence, they are also expected to be connected to future communication networks [35, 36]. Con- sequently, radar sensing and communication may be jointly performed by the BS or access point (AP) using the same hardware to efficiently utilize scarce frequency resources and improve the energy-efficiency of the system [35–37]. As a result, 5G beyond and 6G mobile network architectures are envisaged to support radar sensing in addition to microwave and mmWave communication especially for vehicular and drone networks and localization applications [21, 30, 33, 35, 38, 39]. Taking into account that massive MIMO offers a vast amount of degrees of freedom by beamforming and utilizing the OFDM radar processing techniques [40, 41], it is possible to develop energy-efficient, robust and high-performance dual-functional massive MIMO radar and communication (RadCom) systems [35, 42,43].

Motivated by the aforementioned benefits of massive MIMO and importance of dual- functional RadCom platforms for future wireless networks, this thesis explorers the techniques to enhance the communication capacity, energy efficiency and radar detection performance of massive MIMO under various channel conditions for future communication and radar systems.

1.2 Aims and Objectives

The performance of wireless communication systems is usually restricted by the physical layer due to reaching limitations of the hardware. By employing a large number of antennas, massive MIMO offers substantial gains in both energy and spectral efficiencies and robustness to severe channel conditions. However, channel estimation errors, channel correlation and mutual coupling can restrict its capabilities. Moreover, the base-station could consume significantly high power, especially while operating with ultra-wideband channels.

On the other hand, radio sensing is expected to be a paramount part of future wireless networks, especially in vehicular and drone networks where sensing and communication are both necessary for safe and functional operations. Therefore, this thesis aims to explore channel correlation, channel estimation errors and mutual coupling, and their impact on massive MIMO and provide solutions to mitigate them. Furthermore, it also examines low-resolution ADCs to reduce the power consumption of the network, and investigates dual-functional massive MIMO OFDM radar and communication systems. Consequently, the main objectives of this thesis are:

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CHAPTER 1. INTRODUCTION 25

• To design an antenna array geometry which mitigates the channel correlation and mutual coupling compared to the regular array geometries in order to enhance the channel capacity in line-of-sight indoor massive MIMO communication systems, and examine indoor massive MIMO channels.

• To investigate the performance of 1−bit and low-resolution ADCs in massive MIMO systems in order to reveal the trade-off between the energy and spectral efficiencies and determine the most suitable modulation schemes for each ADC resolution.

• To explore a precoder design for joint downlink communication and radar sensing by exploiting the interference between them to enable dual functionality, and mathematically analyse its performance in terms of radar detection and communication capacity under channel estimation errors.

• To develop energy-efficient precoders for dual-functional RadCom systems by considering practical scenarios where UEs and targets are randomly located, and investigate their spectral and energy efficiencies.

• To study joint uplink communication and radar sensing and mathematically analyse the performance of the system under channel estimation errors and various radar- communication output power ratio.

1.3 Contributions

The major contributions of this PhD thesis are presented in this section. Moreover, some of these contributions have already been published in journals or conference proceedings, and some of them are still under review at the time of writing this thesis and these are given in details in Section 1.5. The major contributions of this thesis are:

• The impact of the antenna array geometry on indoor massive MIMO networks are studied and an antenna array geometry is proposed to reduce the mutual coupling among elements and mitigate the channel correlation in line-of-sight (LOS) channels.

Shifting the locations of the array elements regularly in the array while keeping the array size the same, the diversity among user channels is enriched, and hence, a higher network capacity is achieved. Moreover, delay spread, received power and the channel correlation among users are investigated and a power control algorithm for uplink massive MIMO networks is proposed. These contributions are published in parts in [44, 45].

• The low-resolution ADCs in uplink massive MIMO networks are examined under channel correlation and imperfect channel state information conditions and optimum modulation schemes for each ADC resolution are revealed. Moreover, the square-root-raised- cosine filter (SRRC) is employed to alleviate the quantization errors in low-resolution systems. The energy efficiency of the network with low-resolution ADCs is investigated and the trade-off between the energy and spectral efficiencies with regards to ADC resolution is investigated. This contributions are published in parts in [46,47].

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CHAPTER 1. INTRODUCTION 26

• A precoder for joint downlink massive MIMO communication and OFDM MIMO radar is proposed and its performance in dual-functional vehicular radar and communication systems is investigated. Moreover, analytical capacity expressions under imperfect CSI are derived and verified by simulations. The communication capacity and radar detection performance of the dual-functional RadCom system are examined. These contributions are submitted to a journal and still under review [48].

• RadCom precoder schemes based on convex optimization are proposed to maximize the communication sum-capacity and energy efficiency of the system while maintain- ing the desired radar detection performance and minimum capacity per UE require- ments. We investigated the sum-capacity and energy-efficiency of this architecture employing different precoding schemes with regards to various systems parameters such as communication and radar output powers, number of UEs and number of antennas. Furthermore, realistic channel conditions consisting of randomly located UEs and targets are considered to examine their performance in vehicular networks. These contributions are submitted to a journal and still under review [49].

• A joint uplink massive MIMO communication and OFDM radar is proposed which exploits the channel diversity between UEs and targets to communicate with multiple uplink UEs while sensing the environment. The zero-forcing (ZF) and ZF successive interference cancellation (ZF-SIC) receivers are employed to separate the communication data and radar returns. It is shown that cancelling the self-interference is vital to achieving a high communication capacity and radar detection performance. The analytical uplink capacity equations are derived and verified by simulations. Some of these contributions are published in parts in [43].

The contributions of each chapter are also presented in details in the chapters.

1.4 Thesis Organisation

The remainder of the thesis is organised as follows:

Chapter 2 presents the fundamentals and background of wireless communication and multiple antenna systems. This includes wireless channels, fading models, multiple antenna communication systems, OFDM modulation, antenna properties.

Chapter 3 presents a literature review of massive MIMO communication and OFDM radars including channel estimation, receiver and precoder techniques, and OFDM radar processing.

Chapter 4 presents the antenna array design and channel measurements for indoor massive MIMO networks and compares two different antenna arrays by analysing the ex- perimental channel data obtained through a prototyped indoor multi-user communication network and performing system-level massive MIMO simulations based on this data. This chapter also examines the importance of power control in uplink massive MIMO communication network by utilizing the measured channel data.

Chapter 5 studies low-resolution ADCs in uplink massive MIMO network to improve energy efficiency and reveals the optimum QAM modulation schemes for each ADC quant-

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CHAPTER 1. INTRODUCTION 27 ization bit. Furthermore, it explores the trade-off between the energy and spectral efficiencies depending on the ADC resolution.

Chapter 6 proposes a precoder for joint massive MIMO OFDM downlink communication and radar systems to simultaneously communicate with multiple downlink users while sensing the environment at the same time and frequency resources. It also derives the analytical capacity expressions and examines the performance of the RadCom system under channel estimation error and various conditions.

Chapter 7 investigates the energy and spectral efficiencies of downlink RadCom systems with proposed precoder schemes based on convex optimization and beam power allocation.

It considers realistic vehicular communication channel conditions where UEs and targets are randomly located in the network.

Chapter 8 proposes a dual functional massive MIMO uplink communication and radar sensing architecture for vehicular networks based on successive interference cancellation. It examines the communication sum-capacity and radar detection performance of the uplink RadCom under various channel conditions and reveals the trade-off between them.

1.5 List of Publications

Several research articles and conference proceedings based on this PhD study have been published or have been under review. These publications are listed as follows:

1.5.1 Published Journal Articles

1. M. Temiz, E. Alsusa, M. W. Baidas, "A Dual-Functional Massive MIMO OFDM Com- munication and Radar Transmitter Architecture," IEEE Transactions on Vehicular Technology, 2020.

2. M. Temiz, E. Alsusa, L. Danoon and Y. Zhang, "On the Impact of Antenna Array Geometry on Indoor Wideband Massive MIMO Networks," in IEEE Transactions on Antennas and Propagation, 2020.

3. M. Temiz, E. Alsusa and L. Danoon, "Impact of imperfect channel estimation and antenna correlation on quantised massive multiple-input multiple-output systems," in IET Communications, vol. 13, no. 9, pp. 1262-1270, 4 6 2019.

1.5.2 Published Conference Proceedings

1. M. Temiz, E. Alsusa and L. Danoon, "A Receiver Architecture for Dual-Functional Massive MIMO OFDM RadCom Systems," 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, 2020, pp. 1-6.

2. M. Temiz, Y. Zhang, E. Alsusa and L. Danoon, "Investigation of Channel Correlation in Indoor Wideband Massive MIMO Systems," 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, Atlanta, GA, USA, 2019, pp. 1577-1578.

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CHAPTER 1. INTRODUCTION 28 3. M. Temiz, E. Alsusa and L. Danoon, "Quantized Massive MIMO Networks Under Channel Correlation and CSI Mismatch," 2019 European Conference on Networks and Communications (EuCNC), Valencia, Spain, 2019, pp. 331-336.

1.5.3 Submitted Journal Articles

1. M. Temiz, E. Alsusa, M. W. Baidas, "Optimized Precoders for Massive MIMO OFDM Based Radar and Communication Systems," submitted to IEEE Transactions on Com- munications, 2020.

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Chapter 2 Fundamentals and Background

I

n this chapter, the basics of wireless communication and large-scale antenna systems (massive MIMO) with widely used fundamental techniques are presented. In large- scale antenna systems, the BS is equipped with a large number of antennas and each antenna has a separate RF chain in order to transmit independent signals. By utilizing these large- scale antennas, the BS beamforms or precodes data onto the UEs by exploiting the spatial channel diversity among them during the downlink. Moreover, the BS receives simultaneously transmitted signals by multiple UEs during uplink by exploiting the estimated channel information. For both uplink and downlink, the channel state information between the UEs and BS is essential for massive MIMO communication. Moreover, since the channel estimation is valid only during the coherence block, orthogonal frequency-division multiplexing (OFDM) is the essential technique employed in massive MIMO systems as in other modern communication systems. Because OFDM divides the entire bandwidth into narrowband subcarriers each of which is narrower than the coherence bandwidth, and hence, they have flat fading channel responses. This makes the channel estimation valid in each subcarrier during the coherence time. This chapter briefly presents the fundamentals and background of communication systems.

2.1 Orthogonal Frequency Division Multiplexing (OFDM)

This section briefly presents OFDM and its implementation in communication systems.

OFDM is widely used in wireless communication systems since it allows efficient utilization of wideband channels and it minimizes the impact of multipath propagation which might cause inter-symbol interference. OFDM divides the wideband channels into multiple subcarriers which have flat-fading characteristics.

2.1.1 OFDM Transmission

It is well-known that the channel response varies through the spectrum since the channel characteristics are dependent on the frequency and the environment. Moreover, it is not possible to accurately estimate the channel response of a wideband channel since the coherence

29

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CHAPTER 2. FUNDAMENTALS AND BACKGROUND 30

-10 -8 -6 -4 -2 0 2 4 6 8 10

Subcarrier Index 0.4

0.5 0.6 0.7 0.8 0.9 1

Normalized Amplitude

Subcarrier sidelobes Subcarrier peaks

Figure 2.1: An illustration of OFDM subcarriers in the frequency domain.

bandwidth would be much narrower. If this wide frequency band is divided into subchannels which are sufficiently narrow (i.e., narrower than the coherence bandwidth of the network) to provide nearly constant frequency response, then it is possible to estimate the channel response of each subchannel by utilizing pilot symbols. Furthermore, OFDM is also an effective and well-known solution to overcome frequency selective channels in communication systems [50]. Figure 2.1 illustrates the OFDM subcarriers in the frequency domain, which are mutually orthogonal. Assuming that the total bandwidth is BW and the number of subchannels is Nsc, the bandwidth of each subchannel is given by ∆f = BW/Nsc. Each subchannel with ∆f bandwidth is associated with a sinusoidal carrier, namely subcarrier, which is defined by [51]

sn(t) = cos (2πfnt) , n = 0, 1, . . . , N − 1, (2.1) where sn(t)denotes the nth subcarrier with fncentre frequency. The orthogonality between two subcarriers (e.g., between the nth and ith subcarriers) can be shown as

Z T 0

cos (2πf_nt + φ_n) cos (2πf_it + φ_i) dt = 0, (2.2) where φn and φi denote the phases of the signals [51].

2.1.2 OFDM Modulation and Demodulation

OFDM signals can be created via inverse discrete Fourier transform (IDFT) from PSK or QAM symbols and its implementation is efficiently performed via the inverse fast Fourier transform (IFFT). On the receiver side, the demodulation of the OFDM signals are performed via discrete Fourier transform (DFT) or fast Fourier transform (FFT). Assuming that xl[k]denotes the lth QAM or PSK symbol transmitted in the kth subcarrier, where l = 1, 2, . . . , L and k = 1, 2, . . . N, the lth continuous-time baseband OFDM signal over N subcarriers is given by

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x_l(t) =

∞

X

l=0 N −1

X

k=0

X_l[k] e^j2πf^k^(t−lT^sym⁾, (2.3) where Tsymdenotes the symbol duration and Xl[k]denotes lth transmit symbol at the kth subcarrier. This signal can be sampled at t = lTsym+ nT_s with Ts = T_sym/N, and the discrete-time OFDM symbol is obtained as

xl[n] =

N −1

X

k=0

Xl[k] e^j2πkn/N, (2.4)

where n = 0, 1, . . . , N −1 denotes the samples [52]. This equation also expresses the N−point IDFT of the data symbols and it is efficiently computed via IFFT in modern communication transceivers.

Figure 2.2 illustrates the QAM-OFDM transmitter and receiver of a single-input single- output system. In the transmitter side, the generated data is firstly converted into parallel data streams and then each of streams is modulated with QAM. Subsequently, these modulated subcarriers are converted into time-domain signals via fast Fourier transform and cyclic-prefix is added to each symbol and then they are combined and converted into analogue signals and transmitted by the RF chain and the antenna. On the receiver side, received signals are converted into baseband digital signals by the RF chain and ADC.

Then, the CP is removed and the signals are converted into parallel data streams and each of these streams is demodulated and after that all demodulated data are combined to obtain the transmitted data.

The CP is created by adding the end of the symbols to in front of them to provide guard time between the symbols. The CP is added to each symbols in OFDM systems to prevent the inter-symbol interference (ISI) which is caused by the delay in the received signals and this delay occurs mainly due to the multi-path propagation. Hence, the duration of the CP (TCP) must be longer than the multi-path delay spread (TD,max) of the communication network i.e TCP ≥ TD,maxto avoid ISI [50]. Another important protection employed in OFDM systems is placing virtual guard bands (VCs) in some subcarriers, i.e., virtual carriers, to avoid adjacent channel interference that occurs due to the out-of-band radiation [52].

2.2 Wireless Channels

Propagation of electromagnetic waves is affected by many factors such as carrier frequency, propagation environment or velocity of the transmitter or receiver. In consequence of these factors, wireless channels are highly dynamic and unpredictable, and hence, they are widely statically modelled while analysing the performance of the wireless systems. Electromag- netic waves encounter three physical phenomena while propagating through the propagation environment, which are listed as [52]:

1. Reflection corresponds to that electromagnetic waves are reflected from the surface if they impinge on an object with large dimensions compared to the wavelength of the waves.

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Tra

QAM Modulation Serial-to-

Parallel Converter

Inverse FFT

Add Cyclic- Preﬁx and Parallel-to- Serial Converter

Input Data DAC TX RF

Chain

QAM Demodulation Parallel-to-

Serial Converter

FFT

Remove Cyclic- Preﬁx and

Serial-to- Parallel Converter

ADC RX RF

Chain Output Data

OFDM Transmitter

OFDM Receiver

Figure 2.2: OFDM modulation and demodulation.

2. Diffraction refers to various phenomena which occur when an electromagnetic wave encounter small obstacles compared to the wavelength of the waves, sharp corners or slits.

3. Scattering refers to a process in which an electromagnetic wave is scattered by small particles compared to the wavelength in the path and re-radiated with different phases and angles.

Consequently, the propagation of an electromagnetic wave is a rapidly varying and extremely complicated process to precisely model. Thus, the propagation of electromagnetic waves is usually statistically modelled as stochastic processes to reflect their tremendously complex and unpredictable behaviours. While modelling the propagation of electromagnetic waves for communication systems, all of these consequences are considered mainly in two categories:

large-scale fading and small-scale fading.

2.2.1 Free-space Propagation

Free-space propagation model considers that the transmitter and receiver are in a free-space, thus reflection, diffraction or scattering do not occur. In his model, the received power by an antenna in the free-space according to the Friis transmission equation is given by [51],

P_r= P_tG_rG_t

c₀ 4πf d

²

| {z }

=

P_L

P_tG_rG_t

λ 4πd

²

(2.5)

where Ptis the transmitted power, Grand Gtdenote receiver and transmitter antenna gains, respectively, d is the distance between the transmitter and the receiver, c0and f indicate the speed of light and the frequency of the wave. PLis the free-space path loss depending on the frequency. In a real propagation environment, the received power significantly differs from the free-space model as it may include other objects which will cause diffraction, scattering and reflections of electromagnetic waves.

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2.2.2 Fading

Free-space propagation does not take into account the situation of the propagation environment which has a vast impact on the propagation of electromagnetic waves. The performance of the wireless communication systems is substantially affected by the channel conditions, hence the propagation environment. Moreover, its effects become more significant as the frequency of the carrier signal increases since the path loss can be extremely high for high frequency signals (e.g., millimetre-waves).

The fading or propagation effect is usually divided into two subcategories as large-scale fading and small-scale fading in wireless communication systems [53]. While the large-scale fading includes the path loss and shadowing which occur over relatively large distances compared to the wavelength of the signals. Path loss can be modelled as a deterministic attenuation with regard to the distance between the transmitter and receiver. Shadowing is mainly caused by the large objects in the propagation environment and generally considered as a random variable in the propagation models. The log-normal distribution is commonly used to model the shadowing which causes slowly varying randomness over time in the received power. On the other hand, the small-scale fading introduces rapid fluctuations occurring in the received power over a short time. Small-scale fading is mainly caused multi-path propagation such that the multiple copies of the same signal may destructively or constructively interfere with each other resulting in rapid changes in the amplitude and phase of the received signals [54]. Path loss is a deterministic attenuation depending on only the distance between the transmitter and the receiver. However, small-scale fading produces short-term fluctuations in the received power which are mostly stochastic and depend on the propagation medium, surrounding objects and the multi-paths. Two different statistical distributions are commonly used for modelling small-scale fading, namely, the Rayleigh and Rician distributions. The Rayleigh fading model is suitable for non-line-of-sight (NLOS) propagation which does not include a very strong path between the transmitter and the receiver, and mostly consist of reflected multi-paths. In the case of having a strong line-of- sight (LOS) electromagnetic wave transmission between the transmitter and receiver, the Rician fading model is generally used. In the Rician model, the K−factor of the Rician distribution function determines the ratio between the power of the strong LOS path and the sum of the reflected paths. Furthermore, delays between multi-paths occur during the propagation of waves due to the reflections, these delays must also be taken into account while considering NLOS channels.

It is common to employ Rayleigh fading channels in microwave massive MIMO communication system models where the UEs are randomly located in cells and a rich scattering of the waves is generally observed [17]. The Rician fading may be employed in millimetre-wave systems where the cell size is relatively small and the communication is mainly provided by LOS links [55].

2.2.3 Path Loss Models

The free-space path loss model is not valid in most applications due to having objects around the transmit and receive antennas. On the other hand, characterization of electromagnetic

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CHAPTER 2. FUNDAMENTALS AND BACKGROUND 34 propagation is very complex to accurately model as it depends on a massive number of variables depending on the propagation environment. Therefore, simplified path loss models based on empirical measurements are developed. A simplified path-loss model is given in [53]

as

P_r[dBm] = Pt[dBm] − 20log10

4πd0

λ

− 10log10

d d₀

^γP L

, (2.6)

where d0 denotes the reference distance, d denotes the distance between the transmit and receive antennas and γP Lis the path loss component which is related to propagation environment. For instance, typical values of γP L are 3.7 − 6.5 for urban macrocells or 1.6 − 3.5 for office buildings [53]. There are also other path loss models such as Okumura’s model, Hata model or COST231 Hata model. These models were created by statistically analysing the empirical measurement data. These models provide more accurate results in specific frequency ranges and in particular environments. More complex propagation or channel models were also studied for microwaves and mmWaves by conducting channel measurements and complex statistical propagation models in [56–58].

2.2.4 Coherence Bandwidth and Coherence Time

While wireless communication channels rapidly vary due to the fast-fading, they can be observed as static over a very short time and in a very narrow bandwidth. This situation is known as flat fading and channel estimation in massive MIMO systems is valid during the flat fading or in each coherence block [59,60]. The channel response is constant in each coherence block which is limited by the coherence time and the coherence bandwidth. In other words, the estimated channel in MIMO systems is valid in a specific bandwidth during a specific time.

Coherence bandwidth is related to the maximum Doppler shift of the network and it is practically calculated by [61]

Tc=

s 9

16πf_D,max² ≈ 0.423

f_D,max, (2.7)

where fD,max is the maximum Doppler shift allowed in a wireless communication network setup. The estimated channel is valid during the coherence time, thus, the communication should be completed in this time using the estimated channel. Accordingly, when the velocity of the transmitter or receiver is high, the coherence time will be shorter, and hence, the channel estimation would need to be more frequently performed. On the other hand, coherence bandwidth is related to the delay spread of the multi-paths, and given by [53]

Bc= 1 TDelay

, (2.8)

where TDelay denotes the delay spread. Note that the subcarrier spacing of OFDM should be shorter than the coherence time, i.e., ∆f ≤ Bcto ensure that each subchannel has a flat fading characteristic. The size of the coherence block, where the channel estimation is valid, is then calculated by CB= T_c× Bc [60].

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2.3 Multi-antenna Channels

When multiple antennas are employed at the transmitter or receiver, spatial channel correlation is observed between the antenna channels. This reduces the capacity of MIMO systems [60]. On the other hand, when the channels are uncorrelated, the capacity of MIMO systems reaches is maximum, and hence, this channel condition is called favourable propagation [59]. If the channel of the jth and kth UEs are orthogonal, the following condition holds

Eg^H_j gk = 0, (2.9)

where gj, g_k ∈ C^{M ×1} and M denotes the number antennas at the base station. Another important property of large-scale antenna channels is the channel hardening which decreases small-scale fading and makes channels behave almost deterministically. Therefore, only large-scale fading can be considered to simplify the channel estimation and power allocation schemes [62]. Channel hardening mitigates the adverse effects of small-scale fading especially when the number of antennas is large [60]. The small-scale fading in multi-antenna channels is generally modelled as independent and identically distributed Gaussian random variable.

For instance, the channel matrix of the kth UE can be modelled as a complex-valued random vector of which element is given by

gm,k∼ CN (0, βk) , (2.10)

between the kth UE and the mth BS antenna, where βk denotes the variance of the channel gain of the kth UE. In the case of correlated channels, various channel correlation models such as Kronecker product or exponential correlation models can be employed [63, 64] to introduce a degree of correlation between the channels.

2.4 Antenna Fundamentals

An antenna is a specifically designed device to transmit or receive radio waves by transform- ing electrical signals (guided waves) into electromagnetic waves (radiation) or vice versa [65].

Antenna gain, radiation efficiency, radiation pattern, bandwidth and impedance are the essential parameters to characterize an antenna and they need to be taken into account while designing an antenna. The radiation region of an antenna is generally divided into three subfields as reactive near-field, radiating near-field and far-field [65]. The first two subfields are immediately surrounding areas of the antennas. The radiation pattern of an antenna is usually defined in the far-field where the antennas are considered to operate in communication systems. Objects in the near fields of antennas may interact with the antenna surface currents and significantly affect the radiation pattern and the impedance of the antennas [66].

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2.4.1 Radiation Pattern

The radiation pattern of an antenna (F (θ, φ)) illustrates the radiated power per unit angle (θ, φ) by the antenna in the far-field. An ideal isotropic antenna equally radiates the power in all directions. However, in most applications, the antenna radiation pattern must have narrower beams to focus the electromagnetic energy into a specific direction to reach further distances or to reduce the interference between antennas.

2.4.2 Antenna Directivity, Gain and Efficiency

The directivity (D (θ, φ) ) of an antenna is the ratio of the maximum radiation power intensity P (θ, φ)maxinto a direction to the radiation power intensity P (θ, φ) of an isotropic antenna which radiates the same total power into all directions, given by [65]

D (θ, φ) = P (θ, φ)max

P (θ, φ) = 4πF (θ, φ) R2π

0

Rπ

0 F (θ, φ)sinθ dθ dφ, (2.11) where it is observed that the directivity is a function of the radiation pattern. The directivity of an ideal isotropic antenna is 1, and directivity of all other antennas is greater than 1. For instance, directivity of a short dipole with a 2.67π beamwidth is 1.5 dBi [66].

The gain (G (θ, φ)) of an antenna is the ratio of the radiated power to the power accepted by the antenna terminals from a connected transmitter. Gain and directivity are related to each other, and the ratio between them is known as radiation efficiency. Radiation efficiency is generally degraded by impedance mismatch or other losses which occur on the antenna due to the antenna materials (conductors and dielectrics) or antenna design. The radiation efficiency of an antenna is given by

µrad= G (θ, φ)

D (θ, φ). (2.12)

As an example, Figure 2.3 shows far-field radiation pattern and directivity of a patch antenna which is designed and simulated in CST microwave studio.

2.4.3 Input Impedance and Impedance Matching

Another important parameter is the input impedance for antennas. Input impedance (Zi) varies through the frequency and it has real (Ri) and imaginary (Xi) parts as (Zi = Ri+ jX_i). The input impedance of an antenna is related to a wide range of factors such as dielectric and conductor materials, frequency, the shape of the antenna and other objects which are in the near field of the antenna. The imaginary part of the impedance stores reactive energy in the near field and this decreases the efficiency of the antenna [65]. The real part of the impedance comprises radiation resistance and ohmic losses.

For the maximum energy transfer from a feed network to an antenna or vice versa, the input impedance of the antenna must match with the impedance of its feed network. If there exists any impedance mismatch between them, some part of the power will be reflected back to the source. In this case, the total transferred energy to the antenna would be diminished due to the impedance mismatch. Voltage reflection coefficient (Γ), voltage standing wave

PERFORMANCE ENHANCEMENT TECHNIQUES FOR LARGE-SCALE ANTENNA ARRAYS BASED COMMUNICATION AND RADAR SYSTEMS