Simplified Robust Adaptive Detection and Beamforming for Wireless Communications E-Book

Ayman ElNashar

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch
• Veröffentlichungsjahr: 2018

Seitenzahl: 478

Beschreibung

This book presents an alternative and simplified approaches for the robust adaptive detection and beamforming in wireless communications. It adopts several systems models including DS/CDMA, OFDM/MIMO with antenna array, and general antenna arrays beamforming model. It presents and analyzes recently developed detection and beamforming algorithms with an emphasis on robustness. In addition, simplified and efficient robust adaptive detection and beamforming techniques are presented and compared with exiting techniques. Practical examples based on the above systems models are provided to exemplify the developed detectors and beamforming algorithms. Moreover, the developed techniques are implemented using MATLAB--and the relevant MATLAB scripts are provided to help the readers to develop and analyze the presented algorithms. Simplified Robust Adaptive Detection and Beamforming for Wireless Communications starts by introducing readers to adaptive signal processing and robust adaptive detection. It then goes on to cover Wireless Systems Models. The robust adaptive detectors and beamformers are implemented using the well-known algorithms including LMS, RLS, IQRD-RLS, RSD, BSCMA, CG, and SD. The robust detection and beamforming are derived based on the existing detectors/beamformers including MOE, PLIC, LCCMA, LCMV, MVDR, BSCMA, and MBER. The adopted cost functions include MSE, BER, CM, MV, and SINR/SNR.

Leseprobe

Table of Contents

Cover

Dedication

About the Author

About the Companion Website

Chapter 1: Introduction

1.1 Motivation

1.2 Book Overview

Chapter 2: Wireless System Models

2.1 Introduction

2.2 DS‐CDMA Basic Formulation

2.3 Performance Evaluation

2.4 MIMO/OFDM System Model

2.5 Adaptive Antenna Array

2.5 Simulation Software

References

Chapter 3: Adaptive Detection Algorithms

3.1 Introduction

3.2 The Conventional Detector

3.3 Multiuser Detection

3.4 Simulation Results

References

Chapter: 4 Robust RLS Adaptive Algorithms

4.1 Introduction

4.2 IQRD‐RLS Algorithm

4.3 IQRD‐Based Receivers with Fixed Constraints

4.4 IQRD‐based Receiver with Optimized Constraints

4.5 Channel Estimation Techniques

4.6 New Robust Detection Technique

4.7 Systolic Array Implementation

4.8 Simulation Results

4.9 Complexity Analysis

Appendix 4.A Summary of Inverse QR Algorithm with Inverse Updating

Appendix 4.B QR Decomposition Algorithms

Appendix 4.C Subspace Tracking Algorithms

References

Chapter 5: Quadratically Constrained Simplified Robust Adaptive Detection

5.1 Introduction

5.2 Robust Receiver Design

5.3 Geometric Approach

5.4 Simulation Results

5.5 Complexity Analysis

Appendix 5.A Robust Recursive Conjugate Gradient (RCG) Algorithm

References

Chapter 6: Robust Constant Modulus Algorithms

6.1 Introduction

6.2 Robust LCCMA Formulation

6.3 Low‐complexity Recursive Implementation of LCCMA

6.4 BSCMA Algorithm

6.5 BSCMA with Quadratic Inequality Constraint

6.6 Block Processing and Adaptive Implementation

6.7 Simulation Results for Robust LCCMA

6.8 Simulation Results for Robust BSCMA

6.9 Complexity Analysis

References

Chapter 7: Robust Adaptive Beamforming

7.1 Introduction

7.2 Beamforming Formulation

7.3 Robust Beamforming Design

7.4 Cooperative Joint Constraint Robust Beamforming

7.5 Robust Adaptive MVDR Beamformer with Single WC Constraint

7.6 Robust LCMV Beamforming with MBWC Constraints

7.7 Geometric Interpretation

7.8 Simulation Results

7.9 Summary

References

Chapter 8: Minimum BER Adaptive Detection and Beamforming

8.1 Introduction

8.2 MBER Beamformer

8.3 MBER Simulation Results

8.4 MBER Spatial MUD in MIMO/OFDM Systems

8.5 MBER Simulation Results

8.6 Summary

References

Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1 CP type and relevant resource block parameters in LTE UL with SC‐FDMA.

Table 2.2 CP and physical RB parameters in LTE DL with OFDM.

Table 2.3 Delay profiles for E‐UTRA channel models.

Table 2.4 Typical propagation channel models used for LTE.

Table 2.5 How systems handle delay spreads.

Table 2.6 MIMO DL TxM mode.

Table 2.7 MIMO types in LTE.

Chapter 4

Table 4.1 Multiplication complexity comparison of MOE‐IQRD detectors.

Table 4B.1 Gram–Schmidt algorithm.

Table 4B.2 Householder transformation.

Table 4B.3 Givens rotations.

Chapter 5

Table 5.1 Complexity analysis comparison.

Table 5A.1 Summary of the robust RCG algorithm.

Chapter 6

Table 6.1 Initializations for simulated CM‐based detectors.

Chapter 7

Table 7.1 MVDR RAB techniques, their notions of robustness and prior information used.

Chapter 8

Table 8.1 MBER simulation parameters in DS‐CDMA system.

Table 8.2 Computational complexity comparison.

Table 8.3 MBER simulation parameters in MIMO/OFDM system.

List of Illustrations

Chapter 1

Figure 1.1 Summary of the book.

Chapter 2

Figure 2.1 Classification of fading channels.

Figure 2.2 The relation between large‐scale and small‐scale fading.

Figure 2.3Figure 2.3 LTE FDD frame and slot structure for normal CP.

Figure 2.4 OFDM transmitter and receiver.

Figure 2.5 Coding and modulation for transmission of data over a radio link.

Figure 2.6 Throughput of a set of coding and modulation combinations, AWGN channels assumed.

Figure 2.7 Spectral efficiency versus SNIR for baseline E‐UTRA.

Figure 2.8 Discrete time DS‐CDMA System model.

Figure 2.9 I/Q code multiplexing with complex spreading circuit.

Figure 2.10 Impulse response of chip pulse shaping filter with

.

Figure 2.11 Example of impulse response and frequency transfer function of a multipath channel.

Figure 2.12 Channel bandwidth and transmission bandwidth configuration for one LTE carrier.

Figure 2.13 OFDM system block diagram.

Figure 2.14 OFDMA system structure.

Figure 2.15 MIMO system.

Figure 2.16 Massive MIMO gain.

Figure 2.17 Energy and bit allocation for a channel instance.

Figure 2.18 Plane wave incident on a ULA with an AOA of

θ

.

Figure 2.19 Narrowband beamformer.

Figure 2.20 DS/CDMA system model with antenna array.

Chapter 3

Figure 3.1 The conventional single user DS/CDMA detector: a bank of MFs.

Figure 3.2 Multiuser detection techniques.

Figure 3.3 Block diagram of PLIC structure.

Figure 3.4 MSE for MF, ZF, and MMSE detectors versus snapshot.

Figure 3.5 Average BER versus SNR for the MF, ZF, and MMSE detectors.

Figure 3.6 MSE of MOE with single constraint for three different scenarios.

Figure 3.7 Output SINR for MOE detector with equal gain channel, optimized channel, and optimum channel.

Figure 3.8 BER for MOE detector with equal gain channel, optimized channel, and optimum channel.

Figure 3.9 Output SINR versus SNR for MOE detector with five channel estimators and the MMSE detector with perfect power control.

Figure 3.10 Output SINR versus SNR for MOE detector with five channel estimators and the MMSE detector with −10 dB near–far effect.

Figure 3.11 Adaptive LCCMA detector with/without whitening under equal power: (a) BER and (b) output SINR.

Figure 3.12 Adaptive LCCMA detector with/without whitening under near–far effect: (a) BER and (b) output SINR.

Chapter 4

Figure 4.1 Block diagram of PLIC structure.

Figure 4.2 Configuration of linearly constrained adaptive‐array beamformer.

Figure 4.3 Systolic array implementation and processing cells for inverse updating of the LC‐IQRD‐RLS beamforming.

Figure 4.4 Systolic array implantation for PLIC‐MOE‐IQRD algorithm.

Figure 4.5 Systolic array implementation for direct‐MOE‐IQRD with max/min.

Figure 4.6 Definitions of cells used in the systolic implementations.

Figure 4.7 Output SINR for IQRD-based detectors versus snapshots.

Figure 4.8 BER for IQRD‐based detectors versus snapshots.

Figure 4.9 Output SINR of optimal MOE‐IQRD with different subspace tracking algorithms.

Figure 4.10 BER of optimal MOE‐IQRD with different subspace tracking algorithms.

Figure 4.11 SINR versus iterations bits for various MOE detectors at 20 dB SNR.

Figure 4.12 BER versus iterations bits for various MOE detectors at 20 dB SNR.

Figure 4.13 Output SINR of MOE‐IQRD w. max/min & QC for different constrained values.

Figure 4.14 BER of MOE‐IQRD w. max/min & QC for different constrained values.

Figure 4.15 Comparison between MOE‐IQRD w. max/min & VL and MOE‐RLS w. VL.

Figure 4.16 Complexity analysis versus detector length at fixed channel length.

Figure 4.17 Complexity analysis versus channel length at fixed detector length.

Figure 4B.1 Architecture of complex Givens rotation.

Chapter 5

Figure 5.1 MOE‐RLS w. QC (real roots).

Figure 5.2 MOE‐RLS w. QC (imaginary roots).

Figure 5.3 Geometric interpretation for simplified VL technique.

Figure 5.4 SINR versus iterations for the first (ideal) initializations scenario.

Figure 5.5 BER versus iterations for the first (ideal) initializations scenario.

Figure 5.6 SINR versus iterations for the second initialization scenario.

Figure 5.7 BER versus iterations for the second initialization scenario.

Figure 5.8 SINR versus iterations for the third initialization scenario.

Figure 5.9 BER versus iterations for the third initialization scenario.

Figure 5.10 SINR versus iterations for the fourth initialization scenario.

Figure 5.11 BER versus iterations for the fourth initialization scenario.

Figure 5.12 SINR versus iterations for the fifth scenario.

Figure 5.13 BER versus iterationsfor the fifth scenario.

Figure 5.14 SINR for the MOE‐RSD Detectors with different step‐size values.

Figure 5.15 BER for the MOE‐RSD Detectors with different step‐size values.

Figure 5.16 SINR for the MOE‐RSD Detectors with different alpha values.

Figure 5.17 BER for the MOE‐RSD Detectors with different alpha values.

Figure 5.18 SINR for the MOE‐RSD w. QC Detector with different constrained values.

Figure 5.19 BER for the MOE‐RSD w. QC detector with different constrained values.

Figure 5.20 Multiplication complexity versus detector length.

Chapter 6

Figure 6.1 SINR versus iterations for different CM‐based detectors.

Figure 6.2 BER versus iterations for different CM‐based detectors.

Figure 6.3 SINR versus block iterations for first scenario.

Figure 6.4Figure 6.4 BER versus block iterations for first scenario.

Figure 6.5Figure 6.5 5 SINR versus block iterations for second scnario.

Figure 6.6 BER versus block iterations for second scenario.

Figure 6.7 Computational complexity of the developed robust detectors versus detector length.

Figure 6.8 Computational complexity versus detector length.

Figure 6.9 Computational complexity versus detector length.

Chapter 7

Figure 7.1 Geometric representation for robust Capon beamforming with ellipsoidal constraint.

Figure 7.2 Geometric interpretation of the robust WC beamformer.

Figure 7.3 Five‐element uniform linear array with one source and two jammers.

Figure 7.4 Output SINR versus snapshot for first scenario.

Figure 7.5 Mean squared error versus snapshots.

Figure 7.6 Signal of interest power versus snapshot.

Figure 7.7 Beampatterns of the presented beamformers.

Figure 7.8 Beampatterns of the presented beamformers.

Figure 7.9 Output SINR versus snapshot for the second scenario.

Figure 7.10 Output SINR versus noise power with 0.03π mismatch angle.

Figure 7.11 Output SINR versus noise power with 0.06π DOA mismatch.

Figure 7.12 Output SINR versus snapshot for non‐coherent stationary moving scenario.

Figure 7.13Figure 7.13 Output SINR versus snapshot for coherent stationary moving scenario.

Figure 7.14 Output SINR versus snapshot for non‐stationary moving scenario.

Figure 7.15 SINR versus mismatch angle.

Figure 7.16 Output SINR versus snapshot index for the first scenario.

Figure 7.17 Steady state array beam patterns versus AOI (in radians) for the first scenario.

Figure 7.18 Output SINR versus noise power for the first scenario with training data size

N

= 50.

Figure 7.19 WC parameters of the robust MVDR‐WC/proposed beamformer at

.

Figure 7.20 Effect of WC parameter

on the output SINR of the first scenario.

Figure 7.21 WC parameters of the robust MVDR‐WC/proposed beamformer at

.

Figure 7.22 WC parameters of the robust MVDR‐WC/proposed beamformer at

.

Figure 7.23 WC parameters of the modified robust MVDR‐WC/proposed beamformer at

.

Figure 7.24 Output SINR versus snapshot index for the modified robust MVDR‐WC/proposed beamformer with the parameters of the first scenario.

Figure 7.25 Output SINR versus snapshot index for the second scenario.

Figure 7.26 Output SINR versus snapshot index for the third scenario.

Chapter 8

Figure 8.1 MMSE versus BER cost function.

Figure 8.2 BER vs SNR for selected algorithms minimizing the BER; equal power distribution.

Figure 8.3Figure 8.3 BER vs SNR for selected algorithms minimizing the BER; desired user power 10 dB below interferers.

Figure 8.4Figure 8.4 BER vs SNR for selected algorithms minimizing the BER; equal power distribution.

Figure 8.5 BER vs SNR for selected algorithms minimizing the BER; desired user power 10 dB below interferers.

Figure 8.6 BER vs SNR for AMBER family algorithms and SD‐MMSE algorithm; equal power distribution.

Figure 8.7 BER vs SNR for LMBER family algorithms and LMS‐MMSE algorithm; equal power distribution.

Figure 8.8 BER vs SNR for three algorithms minimizing BER cost function (LMBER, Newton‐LMBER, BSMBER) and one minimizing MSE cost function (DMI); equal power distribution.

Figure 8.9 BER versus SNR for three algorithms minimizing BER cost function (LMBER, Newton‐LMBER, BSMBER) and one minimizing MSE cost function (DMI); desired user power 10 dB below interferers.

Figure 8.10 BER vs SNR for AMBER family and SD‐MMSE algorithms; equal power distribution at SNR = 30 dB.

Figure 8.11 BER vs iterations for LMBER family and LMS‐MMSE algorithms; equal power distribution at SNR = 30 dB.

Figure 8.12 BER versus Iterations for three algorithms minimizing BER cost function (LMBER, Newton‐LMBER, BSMBER) and one minimizing MSE cost function (DMI); equal power distribution at SNR = 15 dB.

Figure 8.13 BER versus iterations for three algorithms minimizing BER cost function (LMBER, Newton‐LMBER, BSMBER) and one minimizing MSE cost function (DMI); desired user power 10 dB below interferers at SNR = 15 dB.

Figure 8.14 BER versus iterations for three algorithms minimizing BER cost function (LMBER, Newton‐LMBER, BSMBER) and one minimizing MSE cost function (DMI); equal power distribution at SNR = 30 dB.

Figure 8.15 BER versus iterations for three algorithms minimizing BER cost function (LMBER, Newton‐LMBER, BSMBER) and one minimizing MSE cost function (DMI); desired user power 10 dB below interferers at SNR = 30 dB.

Figure 8.16 BER versus number of users for three algorithms minimizing the BER cost function (LMBER, Newton‐LMBER, BSMBER) and one minimizing the MSE cost function (DMI) with equal power distribution at SNR = 30 dB.

Figure 8.17 BER versus number of users for three algorithms minimizing BER cost function (LMBER, Newton‐LMBER, BSMBER) and one minimizing MSE cost function (DMI); desired user power 10 dB below interferers at SNR = 30 dB.

Figure 8.18 BER vs iterations for AMBER family and MMSE algorithms; SU and MF as higher and lower steady‐state limits, respectively; equal power distribution at SNR = 30 dB.

Figure 8.19 BER vs iterations for LMBER family and MMSE algorithms; SU and MF as higher and lower steady‐state limits, respectively; equal power distribution at SNR = 30 dB.

Figure 8.20 BER vs iterations for LMBER, Newton‐LMBER, BSMBER and MMSE algorithms; SU and MF as higher and lower steady‐state limits, respectively; equal power distribution at SNR = 30 dB.

Figure 8.21 BER vs SNR for AMBER family and MMSE algorithms; SU and MF as higher and lower steady‐state limits, respectively; equal power distribution at SNR = 30 dB.

Figure 8.22 BER vs SNR for LMBER family and MMSE algorithms; SU and MF as higher and lower steady‐state limits, respectively; equal power distribution at SNR = 30 dB.

Figure 8.23 BER vs SNR for LMBER, Newton‐LMBER, BSMBER and MMSE algorithms; SU and MF as higher and lower steady‐state limits, respectively; equal power distribution at SNR = 30 dB.

Guide

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Simplified Robust Adaptive Detection and Beamforming for Wireless Communications

Ayman Elnashar

Emirates Integrated Telecommunications Company (EITC) Dubai UAE

Copyright

This edition first published 2018

© 2018 John Wiley & Sons Ltd

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

The right of Ayman Elnashar to be identified as the authors of this work has been asserted in accordance with law.

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John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA

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While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

Library of Congress Cataloging-in-Publication Data

Names: Elnashar, Ayman, author.

Title: Simplified robust adaptive detection and beamforming for wireless communications / by Ayman Elnashar.

Description: First edition. | Hoboken, NJ : John Wiley & Sons, 2018. | Includes index. |

Identifiers: LCCN 2017060724 (print) | LCCN 2018007145 (ebook) | ISBN 9781118938232 (pdf) | ISBN 9781118938225 (epub) | ISBN 9781118938249 (cloth) |

Subjects: LCSH: Adaptive signal processing. | Beamforming. | Wireless communication systems.

Classification: LCC TK5102.9 (ebook) | LCC TK5102.9 .E36 2018 (print) | DDC 621.382/4-dc23

LC record available at https://lccn.loc.gov/2017060724

Cover design: Wiley

Cover image: © scanrail/istockphoto;

© hakkiarslan/istockphoto

Dedication

This book is dedicated to the memory of my parents (God bless their souls). They gave me the strong foundation and unconditional love, which remains the source of motivation and is the guiding light of my life.

Also, this book is dedicated to my PhD supervisors, Prof. Said Elnoubi from Alexandria University and Prof. Hamdi Elmikati from Mansoura University. They have guided and encouraged me during my PhD thesis and inspired me to author this book.

To my dearest wife, your encouragement and patience has strengthened me always.

To my beloved children Noursin, Amira, Yousef, and Yasmina. You are the inspiration!

Finally, I acknowledge the contribution of Tamer Samir from mobily for chapter 8.

– Ayman Elnashar, PhD

About the Author

Ayman Elnashar, PhD, has 20+ years of experience in telecoms industry including 2G/3G/LTE/WiFi/IoT/5G. He was part of three major start‐up telecom operators in MENA region (Orange/Egypt, Mobily/KSA, and du/UAE). Currently, he is Vice President and Head of Infrastructure Planning ‐ ICT and Cloud with the Emirates Integrated Telecommunications Co. “du”, UAE. He is the founder of the Terminal Innovation Lab and UAE 5G innovation Gate (U5GIG). Prior to this, he was Sr. Director – Wireless Networks, Terminals and IoT where he managed and directed the evolution, evaluation, and introduction of du wireless networks including LTE/LTE‐A, HSPA+, WiFi, NB‐IoT and currently working towards deploying 5G network in UAE. Prior to this, he was with Mobily, Saudi Arabia, from June 2005 to Jan 2008 as Head of Projects. He played key role in contributing to the success of the mobile broadband network of Mobily/KSA. From March 2000 to June 2005, he was with Orange Egypt.

He published 30+ papers in wireless communications arena in highly ranked journals and international conferences. He is the author of “Design, Deployment, and Performance of 4G‐LTE Networks: A Practical Approach” published by Wiley & Sons, and “Practical Guide to LTE‐A, VoLTE and IoT: Paving the way towards 5G” to be published in May 2018. His research interests include practical performance analysis, planning and optimization of wireless networks (3G/4G/WiFi/IoT/5G), digital signal processing for wireless communications, multiuser detection, smart antennas, massive MIMO, and robust adaptive detection and beamforming.

About the Companion Website

This book is accompanied by a companion website:

www.wiley.com/go/elnashar49

The website include:

Matlab scripts

1Introduction

1.1 Motivation

This book presents alternative and simplified approaches for the robust adaptive detection and beamforming in wireless communications. The book adopts several system models, including:

DS/CDMA, with and without antenna array

MIMO‐OFDM with antenna array

general smart antenna array model.

Recently developed detection and beamforming algorithms are presented and analyzed with an emphasis on robustness. In addition, simplified and efficient robust adaptive detection and beamforming techniques are developed and compared with existing techniques. The robust detectors and beamformers are implemented using well‐known algorithms including, but not limited to:

least‐mean‐square

recursive least‐squares (RLS)

inverse QR decomposition RLS (IQRD‐RLS)

fast recursive steepest descent (RSD)

block‐Shanno constant modulus (BSCMA)

conjugate gradient (CG)

steepest descent (SD).

The robust detection and beamforming methods are derived from existing detectors/beamformers including, but not limited to:

the robust minimum output energy (MOE) detector

partition linear interference canceller (PLIC) detector

linearly constrained constant modulus (CM) algorithm (LCCMA),

linearly constrained minimum variance (LCMV) beamforming with single constraint,

minimum variance distortionless response (MVDR) beamformer with multiple constraint

block Shanno constant modulus algorithm (BSCMA) based detector/beamformer

adaptive minimum bit error rate (BER) based detectors.

The adopted cost functions include the mean square error (MSE), BER, CM, MV and the signal‐to‐noise or signal‐to‐interference‐plus‐noise ratios (SINR/SNR). The presented robust adaptive techniques include:

quadratic inequality constraint (QIC)

diagonal loading techniques

single and multiple worst‐case (WC) constraint(s)

ellipsoidal constraint

joint constraints

Detailed performance analysis in terms of MSE, SINR, BER, computational complexity, and robustness are conducted for all the presented detectors and beamformers. Practical examples based on the above system models are provided to exemplify the developed detectors and beamforming algorithms. Moreover, the developed techniques are implemented using Matlab and the relevant Matlab scripts are provided to allow the readers to develop and analyze the presented algorithms. The developed algorithms will be presented in the context of DS/CDMA, MIMO‐OFDM, and smart antenna arrays, but they can be easily extended to other domains and other applications. Figure 1.1 provides a high‐level description of the book.

Figure 1.1 Summary of the book.

Recently, robust adaptive detection/beamforming has become a hot topic. Researchers seek to provide robustness against uncertainty in the direction of arrival (DOA) or the signature waveform, accuracy errors, calibration errors, small sample sizes, mutual coupling in antenna arrays, and so on. The major concern with the robust algorithms is the compromises involving robustness, complexity, and optimality. This book is aims to efficiently address this concern by presenting alternative and simplified approaches for robust adaptive detection and beamforming in wireless communications systems. The presented algorithms have low computational complexity while offering optimal or close‐to‐optimal performance and can be practically implemented. Wireless communication applications using DS/CDMA, MIMO‐OFDM, and smart antenna systems are presented to demonstrate their robustness and to compare their complexity with established techniques and optimal detectors/beamformers.

The book presents and addresses current hot topics in adaptive signal processing: robustness and simplified adaptive implementation. It presents simplified approaches that add robustness to adaptive signal processing algorithms, with less computational complexity, while maintaining optimality. In addition, the presented algorithms are illustrated with practical examples and simulation results for major wireless communications systems, including DS/CDMA, MIMO‐OFDM, and smart antenna systems. Moreover, Matlab scripts are provided for further analysis and development. The reader can easily extend the techniques and approaches in this book to other areas and to different applications.

With the growth of mobile communication subscribers, the introduction of high data‐rate services, and the overall increase in user traffic, new ways are needed to increase the capacity of wireless networks. Smart antennae, MIMO and beamforming are some of the most promising technologies now being exploited to enhance the capacity of the cellular system. In wireless networks, the traditional omni and directional antennae of a base‐station cause higher interference than necessary. Additionally, they are wasteful, as most transmitted signals will not be received by the target user. Adaptive antennas are a multidiscipline technology area that has exhibited growth steadily over the last four decades, primarily due to the impressive advances in the field of digital processing. Exploiting the spatial dimension using adaptive antennae promises impressive increases in system performance in terms of capacity, coverage, and signal quality. This will ultimately lead to increased spectral efficiency and extended coverage, especially for higher‐frequency bands, such as millimetre waves (mmWave), that will be adopted for 5G evolution.

1.2 Book Overview

In Chapter 1, the mathematical models of DS/CDMA and MIMO‐OFDM systems are presented. These form the foundation for the robust adaptive detection and beamforming algorithms that will be presented and/or developed in this book. DS/CDMA and OFDM are used in 3GPP 3G and 4G systems respectively. The 5G system under development by 3GPP will use evolved versions of MIMO‐OFDM. The algorithms presented in this book may fit any of these systems and may also be extended to other systems. The focus of the 3G and 4G evolutions were on mobile broadband, as a result of widespread smartphone adoption. The internet of things (IoT) evolution will lead to billions of devices being connected to the internet and this has directed the 3GPP and mobile communications industry towards narrowband technologies. 3GPP has modified the LTE system to meet the IoT requirements by introducing NB‐IoT. Other proprietary technologies, such as low‐power wide‐area networks, have used narrowband or ultra‐narrowband technologies such as chirp spread spectrum. The focus of this book is not on certain technologies and readers will need to expend some effort in order to apply the detection and beamforming algorithms outlined here to specific systems. The focus of the book is the development and comparative analysis of robust adaptive detection and beamforming algorithms based on simplified system models. All the results in the book are simulated using Matlab and the developed scripts are provided along with the book. The reader may need to slightly modify the scripts depending on the Matlab version. In addition, some algorithms developed by other authors are provided as part of the software package with this book for the purpose of comparative analysis.

In Chapter 3, we will provide a survey of adaptive detection algorithms based on the DS/CDMA model. However, the adaptive techniques that are summarized in this survey can be easily extended to MIMO‐OFDM and smart antenna arrays. The DS/CDMA model is the most complicated system model, because of its need for multiuser interference cancellation and since the channel is frequency selective, as explained in Chapter 2. Despite the various advantages of the DS/CDMA system, it is interference limited due to multiuser interference and it cannot be easily extended to ultra‐broadband systems. A conventional DS/CDMA receiver treats each user separately as a signal, with other users considered as noise or multiple access interference (MAI). A major drawback of such conventional DS/CDMA systems is the near–far problem: degradation in performance due to the sensitivity to the power of the desired user against the power of the interference. A reliable demodulation is impossible unless tight power control algorithms are exercised. The near–far problem can significantly reduce the capacity. Multiuser detection (MUD) algorithms can give dramatically higher capacity than conventional single‐user detection techniques. MUD considers signals from all users, which leads to joint detection. MUD reduces interference and hence leads to a capacity increase, alleviating the near–far problem. Power control algorithms can be used but are not necessary.

Linear receiver design by minimization of some inverse filtering criterion is explained in Chapter 4. Appropriate constraints are used to avoid the trivial all‐zero solution. A well‐known cost function for the constrained optimization problem is the variance or the power of the output signal. An MOE detector for multiuser detection is developed, based on the constrained optimization approach. In an additive white Gaussian environment with no multipath, this detector provides a blind solution with MMSE performance. In Chapter 4, linearly constrained IQRD‐RLS algorithms with multiple constraints are developed and implemented for MUD in DS/CDMA systems. As explained above, the same algorithms can be extended to MVDR beamforming algorithms. Two approaches are considered, the first with a constant constrained vector and the other with an optimized constrained vector. Three IQRD‐based detectors are developed as follows:

a direct form MOE detector based on the IQRD update method with fixed constraints

a MOE detector in the PLIC structure based also on the IQRD‐RLS algorithm

an optimal MOE algorithm built using the IQRD update method and a subspace tracking algorithm for tracking the channel vector.

The constrained vector (estimated channel vector) is obtained using the max/min approach with IQRD‐RLS based subspace tracking algorithms that are analyzed and tested for channel vector tracking.

The recently developed subspace tracking algorithms are tested and analyzed for channel estimation in Chapter 4. These are the fast orthogonal projection approximation subspace tracking (OPAST) algorithm and the normalized orthogonal Oja (NOOja) algorithm. In addition, a fast subspace tracking algorithm based on the Lagrange multiplier methodology and the IQRD algorithm will be developed and adopted for channel vector estimation and tracking. Moreover, a new strategy for combining the max/min channel estimation technique with the robust quadratic constraint technique is proposed anchored in the direct form algorithm. Specifically, a robust MOE detector is developed, based on the max/min approach and QIC on the weight vector norm to overcome noise enhancement at low SNR. A direct form solution is introduced for the quadratically constraint detector with a variable loading (VL) technique employed to satisfy the QIC. Thus, the IQRD algorithm acts as a core to the proposed receivers, which facilitate real‐time implementation through systolic implementation. However, the same algorithms can be easily implemented using fast and robust RLS‐based algorithms.

A robust low‐complexity blind detector is presented in Chapter 5. This is based on a recursive steepest descent (RSD) adaptive algorithm rather than the RLS algorithm and a QIC on the weight vector norm. The QIC is employed to manage the residual signal mismatch and other random perturbations errors. In addition, the QIC will make the noise constituent in the output SINR constant and hence overcome noise enhancement at low SNR. Quadratic constraints have been used in adaptive beamforming for a variety of purposes, such as improving robustness against mismatch and modeling errors, controlling mainlobe response, and enhancing interference cancellation capability. The quadratic constraint will be analyzed along with beamforming algorithms in Chapter 7.

Analogous to the recursive conjugate gradient (RCG) algorithm, a fast RSD algorithm is developed in Chapter 5. A low‐computational complexity recursive update equation for the gradient vector is derived. Furthermore, a variable step‐size approach is introduced for the step‐size update of the RSD algorithm based on an optimum step‐size calculation. The RSD algorithm is exploited to update the adaptive weight vector of the PLIC structure to suppress MAI. The same technique will be extended to MVDR beamforming in Chapter 7. From this similarity, the reader can easily extend the algorithms in this book to other systems and even beyond the realm of wireless communications. From this it can be seen that we have simplified the deployment of the robust techniques, such as quadratic constraints, uncertainty constraints, worst‐case constraint optimization, and constrained optimization.

The drawbacks of diagonal loading techniques are tackled in Chapter 5. An alternative way of robust adaptive detection based on the RSD adaptive algorithm is presented. This involves an accurate technique for precisely computing the diagonal loading level without approximation or eigendecomposition. We combined the QIC with the RSD algorithm to produce a robust recursive implementation with O(N2) complexity. A new optimal VL technique is developed and integrated into the RSD adaptive algorithm. In addition, the diagonal loading term is optimally computed, with O(N) complexity, using a simple quadratic equation. Geometrical interpretations of the scaled projection (SP) and VL techniques, along with RLS and RSD algorithms, are illustrated and analyzed. The performance of the robust detectors is compared with traditional detectors and the former are shown to be more accurate and more robust against signal mismatch and random perturbations. Finally, the presented approach can be reformulated to handle an uncertainty constraint – imposed on the signature waveform in MUD, or on a steering vector in beamforming – such as the ellipsoidal constraint. It can also be exploited with any of the robust approaches to produce a simple recursive implementation.

In Chapter 6, the quadratic inequality constraint is imposed on the weight vector norm of the LCCMA and BSCMA algorithms in order to enhance their performance. The weight norm constraint will control the gradient vector norm, meaning that there is no need to check the gradient vector norm increase in BSCMA. Additionally, the iteration inside the block can continue without affecting algorithm stability due to the weight vector norm constraint. We will investigate the effect of adding a quadratic inequality constraint on the LCCMA and BSCMA algorithms. The proposed VL technique in Chapter 5 is exploited to estimate the optimum diagonal loading value. The LCCMA and BSCMA algorithms are used to update the adaptive vector of the PLIC structure. The PLIC structure with multiple constraints is employed to identify the MAI and hence help in avoiding interference capture. Moreover, the different forms of BS‐CMA algorithms – the block‐conjugate gradient CMA algorithm (BCGCMA) and block gradient descent constant modulus algorithm (BGDCMA) – are investigated as well. The resistance of BSCMA‐based algorithms against the near–far effect is discussed and evaluated.

In Chapter 7, we will present four approaches for robust adaptive beamforming as follows:

Improved recursive realization for robust LCMV beamforming

We first develop an improved recursive realization for robust LCMV beamforming. This includes an ellipsoidal uncertainty constraint on the steering vector. The robust recursive implementation presented here is based on a combination of the ellipsoidal constraint formulation and the variable diagonal loading technique demonstrated in

Chapter 5

. As a consequence, an accurate technique for computing the diagonal loading level without eigendecomposition or SOCP is developed. The geometrical interpretation of the diagonal loading technique is demonstrated and compared with eigendecomposition approach. Note that this approach adopts a spherical constraint on the steering vector to optimize the beamformer output power. Unfortunately, the adaptive beamformer developed here is apt to noise enhancement at low SNR and an additional constraint is required to bolster the ellipsoidal constraint.

Joint constraint approach for a joint robustness beamformer

The second approach is the development of a joint constraint approach for a joint robustness beamformer. A joint constraint approach is presented for joint robustness against steering vector mismatch and unstationarity of interferers. An alternative approach involves imposing an ellipsoidal uncertainty constraint and a quadratic constraint on the steering vector and the beamformer weights, respectively. We introduce a new simple approach to get the corresponding diagonal loading value. The quadratic constraint is invoked as a cooperative constraint to overcome noise enhancement at low SNR. The performance of the robust adaptive schemes developed and other robust approaches are demonstrated in scenarios with steering vector mismatch and several moving jammers.

Beamformer with a single WC constraint

In the third approach, a robust MVDR beamformer with a single WC constraint is implemented using an iterative gradient minimization algorithm. This involves a simple technique to estimate the Lagrange multiplier instead of a Newton‐like algorithm. This algorithm exhibits several merits, including simplicity, low computational load, and no need for either sample‐matrix inversion or eigendecomposition. A geometric interpretation of the robust MVDR beamformer is demonstrated to supplement the theoretical analysis.

LCMV beamformer with MBWC constraints

In the last approach, a robust LCMV beamformer with multiple‐beam WC (MBWC) constraints is developed using a novel multiple‐WC constraints formulation. The optimization problem entails solving a set of nonlinear equations. As a consequence, a Newton‐like method is mandatory to solve the system of nonlinear equations, which yields a vector of Lagrange multipliers. The Lagrange method is used to give the solution.

The traditional MMSE detector is the most popular technique for beamforming. An adaptive implementation of the MMSE can be achieved by minimizing the MSE between the desired output and the actual array output. The LCMV and MVDR beamformers in Chapter 7 are different forms of MMSE detectors. For a practical communication system, it is the BER or block BER, not the MSE performance, that really matter. Ideally, the system design should be based directly on minimizing the BER rather than the MSE. For application in single‐user channel equalization, multiuser detection, and beamforming, it has been shown that the MMSE solution can, in certain situations, be distinctly inferior to the minimum BER (MBER) solution. However, the BER cost function is not a linear function of the detector or the beamformer, making it difficult to minimize. Several adaptive MBER beamformer/detectors implementations are developed in the literature.

It must be stated here that the cost function of the MMSE criterion has a circular shape. This means that we have one global minimum. Hence, convergence can be easily achieved. In contrast, the cost function of the BER is highly nonlinear. This means that during minimization steps we may converge to a local minimum. The MMSE and MBER solutions lead to very different detector weight vectors. Clearly, the MBER design is more intelligent in utilizing the detector's resources. However, special attention is mandatory with the minimization algorithm in order to avoid convergence to a local minimum, and hence the algorithm diverging rather than converging.

Beamforming is a key technology in smart antenna systems, and can increase capacity and coverage and mitigate multipath propagation in mobile radio communication systems. The most popular criterion for linear beamforming is MMSE. However, the MSE cost function is not optimal in terms of the bit error probability performance of the system. In Chapter 8, a class of adaptive beamforming algorithms using direct minimization of the BER cost function is presented. Unfortunately, the popular least minimum BER stochastic beamforming algorithm suffers from low convergence speeds. Gradient Newton algorithms are presented as an alternative. These speed up the convergence rate and enhance performance but only at the expense of complexity. In Chapter 8, a block processing objective function for the MBER is formulated, and a nonlinear optimization strategy that produces the so‐called ‘block‐Shanno MBER’ is developed. A complete consideration of the complexity calculations of the proposed algorithm is given. Simulation scenarios are carried out in a multipath Rayleigh‐fading DS‐CDMA system to explore the performance of the proposed algorithm. Simulation results show that the proposed algorithm offers good performance in terms of convergence speed, steady‐state performance, and even system capacity, compared to other MBER‐ and MSE‐based algorithms.

Finally, we will extend the adaptive filtering algorithms using the concept of spatial multiuser detection in a MIMO‐OFDM system model rather than beamforming in a DS‐CDMA model. As stated above, a fundamental goal in any digital communications system is to directly minimize the BER. Wiener solution based algorithms indirectly minimize the BER by optimizing other cost functions (SNR, SINR, or MSE), which may result in suboptimal BER performance.

2Wireless System Models

2.1 Introduction

In this chapter, mathematical models of DS/CDMA and orthogonal frequency division multiplexing (OFDM) systems are presented. These form the foundation of the robust adaptive detection algorithms that will be presented in this book. DS/CDMA and OFDM are used in 3GPP 3G and 4G systems respectively. The 5G systems under development by 3GPP will use evolved versions of OFDM. The algorithms presented in this book suit any of these systems and may also be extended to other systems.

The focuses of 3G and 4G were on mobile broadband thanks to smartphone adoption. The internet of things (IoT), with billions of devices expected to be connected to the internet, has directed the 3GPP and industry towards narrowband technologies. 3GPP has modified the LTE system to meet IoT requirements by introducing NB‐IoT. Other proprietary technologies in unlicensed bands, known as low‐power wide area (LPWA) networks, have used narrowband or ultra‐narrowband approaches such as SigFox. Other LPWA networks have adopted a wideband CDMA approach based on chirp spread spectrum (CSS). One example is LoRa.

The focus of this book is not on particular commercial technologies, and readers will require some effort in order to match the detection and beamforming algorithms developed in this book to specific practical systems. The focus of the book is on the development and comparative analysis of robust adaptive detection and beamforming algorithms based on simplified system models. All results in the book are simulated using Matlab and the scripts used are provided along with the book. The reader may need to slightly modify the scripts depending on the Matlab version. Also, some algorithms developed by other authors are provided as part of the software package with this book for the purpose of comparative analysis.

Due to the complex nature of CDMA systems, there have been many different formulations of the DS/CDMA model. In this book, we consider a general DS‐CDMA system model, which account for user asynchronism, multipath propagation, and frequency‐selective fading propagation channels. The link from the base station to the mobile station is referred to as the “downlink” and is typically characterized by synchronous data transmission. More challenging for demodulation is the “uplink” from mobile to base station, where different user transmissions are typically asynchronous and of widely disparate power levels. Reliable modulation might also require mitigation of multipath interference, especially in wideband CDMA (WCDMA) schemes where multipath effects can be significant. The robust adaptive receivers/detectors presented and/or developed in this book are suitable for both uplink and downlink scenarios. Important performance measures are formulated for assessment and comparative analysis. DS/CDMA was adopted in 3G/HSPA+ mobile communication systems and IEEE 802.15.4. The main reasons for using CDMA techniques are as follows:

resistance to unintended or intended jamming/interference

sharing of a single channel among multiple users

reduced signal/background‐noise level hampering interception

determination of relative timing between transmitter and receiver

robustness against multipath propagation and frequency selective channels.

Second‐ and third‐generation mobile systems are based on either TDMA or CDMA technologies. Although these technologies can theoretically be extended to next‐generation wireless broadband systems, practical implementation issues and complexities limit their adoption. On the other hand, OFDM offers an easier solution and simple implementation. However, OFDM is not without its issues.

Multipath signal propagation makes the channel response time dispersive; the amount of signal dispersion depends on the environment of operation. For example, the channel dispersion is about 5 µs in typical urban areas and 15–20 µs in rural and hilly terrain. The factor that affects the receiver is the number of resolvable channel taps over the channel dispersion interval. In a TDMA system, it is the ratio of the channel dispersion to signal symbol time. However, in a CDMA system, it is the number of channel taps with strong energy at chip‐time resolution over the channel dispersion period. The channel time dispersion is viewed as frequency‐selective or non‐selective in the frequency domain. A frequency non‐selective channel means the signal, over its entire bandwidth, will have the same effect as due to the multipath channel. This is also called a flat fading channel. In the time domain, the channel is not dispersive relative to its symbol time, and hence there is no intersymbol interference (ISI). In the frequency‐selective channel, the signal will have independent effects over its bandwidth due to the channel, and it is time dispersive relative to its symbol time.

In narrowband TDMA systems, such as GSM, multipath propagation makes the channel frequency non‐selective or less selective, making the receiver less complex. Extending TDMA techniques to broadband systems makes the receiver complexity unmanageable, as the channel becomes highly frequency selective. More specifically, GSM is a 200 kHz channel TDMA system, of 270.833 kHz symbol rate with either binary GMSK or 8‐PSK modulations. The baseband signal uses partial response signalling, which spreads the symbol to three symbol periods. For a typical urban case with about 5 µs channel dispersion, the received signal can have a signal dispersion of about 5 symbol periods, including its partial response signalling. Therefore, a typical GSM receiver requires a 16‐state MLSE (maximum likelihood sequence estimation) equalizer for Gaussian minimum shift keying (GMSK) modulation and an 8‐ or 64‐state DFSE (decision feedback sequence equalizer) for an 8‐PSK EDGE signal. Suppose we want to scale up this technique to a wideband or broadband system by factor of 100; that is, a 20 MHz channel bandwidth like LTE system with 27.0833 MHz symbol rate. For the same amount of channel dispersion, the received symbol will be spread over 200 symbol periods, which means a very frequency‐selective channel. The receiver with an equalizer for 200 channel taps will be impractical to implement due to the very high complexity.

Similarly, WCDMA can also be extended to broadband systems, but its complexity increases, as it requires a larger number of rake receiver fingers. Complexity of a rake receiver, and often its gain, are based on the number of rake fingers the receiver can process. A typical WCDMA rake receiver requires about 5–8 rake fingers for a typical urban channel with channel spread of 5 µs. More advanced receivers, such as generalized rake receivers (G‐rake), require even more fingers, placed around the desired signal, and often called “interference fingers”. Extending WCDMA to a 20 MHz broadband system will require higher chip rates, meaning that it can resolve channel taps with finer resolution. This results in more fingers for the rake receiver with strong signal energy. Therefore, extension of WCDMA/HSPA+ systems to a 20 MHz broadband system requires expansion, by a similar factor, of the number of fingers in the rake receiver, and thus an increase in its detection complexity especially at the mobile side. In addition, the receiver design will be further complicated if we need to add multiple‐input, multiple‐output (MIMO) on top of such complicated receivers; the gain of MIMO will be minimized due to the frequency selective nature and the high number of detector taps. This is the main reason behind the delay in deploying MIMO with 3G/HSPA+ mobile systems while MIMO 2 × 2 has been used with 4G/LTE OFDM based systems from day one and currently MIMO 4 × 4 and MIMO 8 × 8 systems are in development. Moreover, 5G evolution will take MIMO and beamforming to the next level by introducing evolved node B (eNB), with 64 antenna elements. The 3GPP has introduced other ways of extending HSPA+ systems to broadband, based on multicarrier HSPA. Most commercial 3G networks adopt DC‐HSPA+ and have evolved to DC/2C/4C‐HSPA+ by combining 2, 3 and 4 carriers. The three/four carriers provide 63 Mbps/84 Mbps in the DL, respectively. However, it is still challenging to go beyond the dual carrier approach due to the network optimization complexity with each carrier considered as one cell. In addition, DC is mainly deployed in the downlink and its adoption in the uplink is still limited. More particularly, the multicarrier approach in the UL is still not widely deployed. With 3C‐HSPA+ modems, dual carriers in the UL will be supported. The complexity in deploying multicarrier HSPA+ systems has been a motivation to develop and deploy the LTE system.

OFDM has become a most favored technique for broadband wireless systems due to the susceptibility to signal spread under multipath conditions. OFDM can also be viewed as a multicarrier narrowband system, where the whole system bandwidth is split into multiple smaller subcarriers with simultaneous transmission. Simultaneous data transmission and reception over these subcarriers are handled almost independently. Each subcarrier is usually narrow enough that the multipath channel response is flat over the individual subcarrier frequency range; that is, there is a frequency non‐selective fading channel. Therefore, the OFDM symbol time is much larger than the typical channel dispersion. Hence OFDM is inherently susceptible to channel dispersion due to multipath propagation.

The fading channels are illustrated in Figure 2.1. The fading can be mainly classified into two different types: large‐scale fading and small‐scale fading. Large‐scale fading occurs as the mobile moves over a large distance, say of the order of the cell size [52]. It is caused by the path loss of the signal as a function of distance, and shadowing by large objects such as buildings, intervening terrain, and vegetation. Shadowing is a slow‐fading process, characterized by variation of median path loss between the transmitter and receiver in fixed locations. In other words, large‐scale fading is characterized by average path loss and shadowing. On the other hand, small‐scale fading involves the rapid variation of signal levels due to the constructive and destructive interference of multiple signal paths (multipaths) when the mobile station moves over short distances. Depending on the relative extent of a multipath, frequency selectivity of a channel is characterized (say, as frequency‐selective or frequency‐flat) for small‐scaling fading. Meanwhile, depending on the time variation in a channel due to mobile speed (characterized by the Doppler spread), short‐term fading can be classified as either fast fading or slow fading. Large‐scale fading and time‐variance fading are addressed in the link budget of the system. The path loss and shadowing identify the cell radius in the mobile system. Multipath fading is addressed in the receiver design and the multiple access technique deployed. The link budget usually assumes slow‐fading channels. For fast‐fading channels, such as with users inside a high‐speed train, a fast‐fading compensation factor is added to the link budget [52].

Figure 2.1 Classification of fading channels.

The relationship between large‐scale fading and small‐scale fading is illustrated in Figure 2.2 [52]. Large‐scale fading is manifested by the mean path loss, which decreases with distance, and shadowing, which varies along the mean path loss. The received signal strength may be different at two different places, even at the same distance from a transmitter, due to shadowing caused by obstacles on the path. Furthermore, the scattering components incur small‐scale fading, which ultimately gives a short‐term variation of the signal, which has already experienced shadowing. A detailed practical link budget for an LTE system is provided in the literature [53]. This illustrates how all of these fading factors are addressed in the link budget.

Figure 2.2 The relation between large‐scale and small‐scale fading.

In traditional systems (TDMA‐ or CDMA‐based systems), symbol detection is on the samples at either the symbol or chip rate, and the carrier‐to‐interference level only matters at the sampling points. However, OFDM symbol detection requires that the entire symbol duration be free of interference from previous symbols, thus preventing ISI. Even though the OFDM symbol duration is much larger than the channel dispersion, even a small amount of channel dispersion causes some spilling of each OFDM symbol to the next symbol, thus causing some ISI. However, this ISI spillover is limited to only the initial part of the neighboring symbol. Hence this ISI spill‐over at the beginning of each symbol can easily be tackled by adding a cyclic prefix (CP) to each transmit symbol. This prefix extends each symbol by duplicating a portion of the signal at the symbol ends. The prefix is removed at the receiver. The amount of symbol extension – that is, the length of the CPs – is a system design parameter, and is based on the expected signal dispersion in the environment of system operation. For example, the LTE system uses an OFDM symbol of 66 µs plus 5 µs of CP. This means it is susceptible to a maximum signal dispersion of 5 µs due to multipath channel propagation.