Space Modulation Techniques E-Book

Raed Mesleh

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

Seitenzahl: 279

Veröffentlichungsjahr: 2018

Beschreibung

Explores the fundamentals required to understand, analyze, and implement space modulation techniques (SMTs) in coherent and non-coherent radio frequency environments This book focuses on the concept of space modulation techniques (SMTs), and covers those emerging high data rate wireless communication techniques. The book discusses the advantages and disadvantages of SMTs along with their performance. A general framework for analyzing the performance of SMTs is provided and used to detail their performance over several generalized fading channels. The book also addresses the transmitter design of these techniques with the optimum number of hardware components and the use of these techniques in cooperative and mm-Wave communications. Beginning with an introduction to the subject and a brief history, Space Modulation Techniques goes on to offer chapters covering MIMO systems like spatial multiplexing and space-time coding. It then looks at channel models, such as Rayleigh, Rician, Nakagami-m, and other generalized distributions. A discussion of SMTs includes techniques like space shift keying (SSK), space-time shift keying (STSK), trellis coded spatial modulation (TCSM), spatial modulation (SM), generalized spatial modulation (GSM), quadrature spatial modulation (QSM), and more. The book also presents a non-coherent design for different SMTs, and a framework for SMTs' performance analysis in different channel conditions and in the presence of channel imperfections, all that along with an information theoretic treatment of SMTs. Lastly, it provides performance comparisons, results, and MATLAB codes and offers readers practical implementation designs for SMTs. The book also: * Provides readers with the expertise of the inventors of space modulation techniques (SMTs) * Analyzes error performance, capacity performance, and system complexity. * Discusses practical implementation of SMTs and studies SMTs with cooperative and mm-Wave communications * Explores and compares MIMO schemes Space Modulation Techniques is an ideal book for professional and academic readers that are active in the field of SMT MIMO systems.

Leseprobe

Table of Contents

Cover

Dedication

Preface

Chapter 1: Introduction

1.1 Wireless History

1.2 MIMO Promise

1.3 Introducing Space Modulation Techniques (SMTs)

1.4 Advanced SMTs

1.5 Book Organization

Chapter 2: MIMO System and Channel Models

2.1 MIMO System Model

2.2 Spatial Multiplexing MIMO Systems

2.3 MIMO Capacity

2.4 MIMO Channel Models

2.5 Channel Imperfections

Chapter 3: Space Modulation Transmission and Reception Techniques

3.1 Space Shift Keying (SSK)

3.2 Generalized Space Shift Keying (GSSK)

3.3 Spatial Modulation (SM)

3.4 Generalized Spatial Modulation (GSM)

3.5 Quadrature Space Shift Keying (QSSK)

3.6 Quadrature Spatial Modulation (QSM)

3.7 Generalized QSSK (GQSSK)

3.8 Generalized QSM (GQSM)

3.9 Advanced SMTs

3.10 Complexity Analysis of SMTs

3.11 Transmitter Power Consumption Analysis

3.12 Hardware Cost

3.13 SMTs Coherent and Noncoherent Spectral Efficiencies

Chapter 4: Average Bit Error Probability Analysis for SMTs

4.1 Average Error Probability over Rayleigh Fading Channels

4.2 A General Framework for SMTs Average Error Probability over Generalized Fading Channels and in the Presence of Spatial Correlation and Imperfect Channel Estimation

4.3 Average Error Probability Analysis of Differential SMTs

4.4 Comparative Average Bit Error Rate Results

Chapter 5: Information Theoretic Treatment for SMTs

5.1 Evaluating the Mutual Information

5.2 Capacity Analysis

5.3 Achieving SMTs Capacity

5.4 Information Theoretic Analysis in the Presence of Channel Estimation Errors

5.5 Mutual Information Performance Comparison

Chapter 6: Cooperative SMTs

6.1 Amplify and Forward (AF) Relaying

6.2 Decode and Forward (DF) Relaying

6.3 Two‐Way Relaying (2WR) SMTs

Chapter 7: SMTs for Millimeter‐Wave Communications

7.1 Line of Sight mmWave Channel Model

7.2 Outdoor Millimeter‐Wave Communications 3D Channel Model

Chapter 8: Summary and Future Directions

8.1 Summary

8.2 Future Directions

Matlab Codes

A.1 Generating the Constellation Diagrams

A.2 Receivers

A.3 Analytical and Simulated ABER

A.4 Mutual Information and Capacity

References

Index

End User License Agreement

List of Tables

Chapter 03

Table 3.1 SM mapping table for

and 4‐QAM modulation.

Table 3.2 GSM mapping table with

,

and BPSK modulation.

Table 3.3 VGSM with

and BPSK modulation.

Table 3.4 QSSK mapping table for

.

Table 3.5 DSSK mapping table for

achieving a spectral efficiency of

bits Hz

.

Table 3.6 DSM mapping table for

and

‐PSK modulation achieving a spectral efficiency of

bits Hz

.

Table 3.7 DQSM bits mapping and permutation matrices for

transmit antennas.

Table 3.8 STSK mapping table for

,

and BPSK modulation.

Table 3.9 Detailed complexity analysis of SS‐SMTs‐SD.

Table 3.10 Detailed complexity analysis of DS‐SMTs‐SD.

Table 3.11 Hardware items cost in US$.

List of Illustrations

Chapter 01

Figure 1.1 IM system model.

Chapter 02

Figure 2.1 General MIMO system model with

transmit antennas and

receive antennas.

Figure 2.2 Ergodic MIMO capacity for different antenna configurations. Capacity improves with larger antenna configurations.

Figure 2.3 Nakagami‐

joint envelope‐phase pdf behavior for variable

values and

and

.

Figure 2.4

–

Joint PDF for fixed

and variable

.

Value is fixed to 1, while

takes the values of 0.1, 0.5, 2.5, and 10.

Figure 2.5

–

Joint PDF for fixed

and variable

.

Value is varied as 0.1, 0.3, 0.5, and 1 while

is fixed to 0.4.

Figure 2.6

–

Joint PDF for fixed

and variable

and for

.

‐Value is set to 2 and

varies from 0.1, 1, 1.5, and 10.

Figure 2.7

–

Joint PDF for fixed

and variable

and for

.

‐Value is set to 2, and

varies from 0.1, 1, 1.5, and 10.

Figure 2.8

–

Joint PDF for fixed

and variable

and for

.

‐Value is varied from 0.1, 1, 3, and 10, and

is fixed to 0.5.

Figure 2.9

–

Joint PDF for fixed

and variable

.

‐Value is set to 1 and

varies from 0.1, 0.7, 2, and 10.

Figure 2.10

–

Joint PDF for fixed

and variable

.

‐Value varies from 0.1, 1, 3, and 10 and

is fixed to 0.5.

Figure 2.11 Geometry of cluster channel model – SC between transmit/receive signals. Angles

are the mean AOA of cluster, channel tap and the AOA offset of the channel tap.

Figure 2.12 Mutual coupling in MIMO system – a network representation.

Chapter 03

Figure 3.1 SSK system model with single RF‐chain and with

transmit and

receive antennas.

Figure 3.2 An example of SSK constellation diagram with the mapping table for

.

Figure 3.3 GSSK system model with single RF‐chain and with

transmit and

receive antennas.

Figure 3.4 An example of GSSK constellation diagram with the mapping table for

and

.

Figure 3.5 SM system model with single RF‐chain and with

transmit and

receive antennas.

Figure 3.6 An example of SM constellation diagram with

and 4‐QAM modulation.

Figure 3.7 GSM system model with single RF‐chain, multiple RF switches and with

transmit antennas,

active antennas at a time and

receive antennas.

Figure 3.8 QSSK system model.

Figure 3.9 QSM system model with single RF‐chain, two RF switches,

transmit antennas and

receive antennas.

Figure 3.10 An illustration for QSM signal and spatial constellation diagrams.

Figure 3.11 GQSSK system model with illustration for

an

achieving

 bits.

Figure 3.12 GQSM system model with illustration for

,

and

‐QAM achieving 9 bits (s Hz)

.

Figure 3.13 Differential space shift keying system model with the mapping table for

.

Figure 3.14 DQSM system model with arbitrary number of transmit,

, and receive,

, antennas and specific modulation order,

, utilizing single RF‐chain transmitter.

Figure 3.15 An example of rate 1/2 TCM encoder with the state diagram and convolutional encoder.

Figure 3.16 An example of TCM set partitioning for 8‐PSK constellation diagram.

Figure 3.17 TCSM possible transition states for

antennas along with the considered convolutional encoder.

Figure 3.18 Transmitter power consumption for GSM, GQSM, GSSK, and GQSSK MIMO systems. For GSM and GQSM,

is assumed.

Figure 3.19 Transmitter power consumption for SM, QSM, SSK, and QSSK MIMO systems. For SM and QSM,

is assumed. Also, for all SMTs, SPDT RF switches are considered in all systems.

Figure 3.20 Needed number of transmit antennas to achieve a target spectral efficiency for SSK, SM, QSSK, QSM, GSSK, GSM, GQSSK, and GQSM MIMO systems. For SM, QSM, GSM, and GQSM,

is assumed and for all GSMTs

is considered.

Figure 3.21 Transmitter implementation cost for SM, QSM, SSK, and QSSK assuming

and SPDT RF switches.

Figure 3.22 Transmitter implementation cost for GSM, GQSM, GSSK, and GQSSK assuming

and SPDT RF switches.

Figure 3.23 A comparison of achievable spectral efficiencies with variable number of transmit antennas,

, and with

‐QAM modulation for different techniques including DQSM, QSM, DSM, SM, DSSK, and SSK systems.

Chapter 04

Figure 4.1 The derived analytical ABER of SM for MISO Rayleigh fading channels in (4.7) compared simulated ABER for

and

.

Figure 4.2 The derived analytical ABER of SM over MISO Rayleigh fading channels in (4.13) compared to the simulated ABER for

,

, and

.

Figure 4.3 The derived analytical ABER of SM over Rayleigh fading channels in the presence of CSE in (4.22) for MISO and in (4.27) for MIMO compared to the simulated ABER for

,

,

, and

.

Figure 4.4 The derived analytical ABER of QSM over Rayleigh fading channels in (4.34) for MISO and in (4.35) for MIMO compared to the simulated ABER for

,

, and

.

Figure 4.5 The derived analytical ABER of QSM over Rayleigh fading channels in the presence of CSE in (4.37) compared to the simulated ABER for

,

, and

.

Figure 4.6 The derived analytical ABER of SMTs in (4.54) compared with the simulated ABER of SM and QSM over correlated Rayleigh and Nakagami‐

fading channels in the presence of CSE, where

,

, and

.

Figure 4.7 The derived analytical ABER of DSMTs in (4.64) compared with the simulated ABER of DSM and DQSM for

‐QAM,

,

.

Figure 4.8 ABER performance comparison between the SMTs, QSSK, SSK, QSM and SM, and SMX systems over Nakagami‐

fading channels for different number of transmit antennas and modulation orders achieving

 bits with

antennas.

Figure 4.9 ABER performance comparison between the SMTs, QSSK, SSK, QSM and SM, and SMX systems over Rayleigh fading channels for different number of transmit antennas and modulation orders achieving

 bits with

antennas.

Figure 4.10 ABER performance comparison between GSMTs, GQSSK, GSSK, GQSM and GSM, and SMX systems over Rayleigh fading channels for different number of transmit antennas and modulation orders achieving

 bits with

antennas.

Figure 4.11 ABER performance comparison between DQSM with

and

‐QAM, DSM with

and

‐PSK, QSM with

and

‐QAM, SM with

and

‐QAM, and SMX with

and BPSK over Rayleigh fading channels for

 bits and

antennas.

Chapter 05

Figure 5.1 Comparison between the derived capacity in (5.43) and the MIMO capacity (5.24) over Rayleigh, Rician

dB, and Nakagami‐

fading channel, for

.

Figure 5.2 Histogram of the real part of the spatial constellation diagram,

, for SSK with

and

compared to the PDF of the Gaussian distribution.

Figure 5.3 The capacity of SMTs compared to simulated mutual information of SSK over Rayleigh fading channel for

and

.

Figure 5.4 The capacity of SMT compared to simulated mutual information of SSK over Rayleigh, Rician with

 dB, and Nakagami‐

, where

and

.

Figure 5.5 Histogram of the phase of a randomly generated symbols modulated using

‐,16‐, and

‐size PSK modulation compared to the uniform distribution.

Figure 5.6 PDF of 

plotted against histogram of

using

‐PSK modulation over MISO Rayleigh fading channel with

.

Figure 5.7 PDF of 

plotted against histogram of

using

‐PSK modulation over MISO Rician fading channel with

 dB and

.

Figure 5.8 PDF of 

plotted against histogram of

using

complex Gaussian‐distributed symbols over MISO Rayleigh fading channel with

.

Figure 5.9 PDF of 

plotted against histogram of

using

complex Gaussian‐distributed symbols over MISO Rician fading channel with

 dB and

.

Figure 5.10 The capacity of SMT compared to the simulated mutual information of SM using PSK constellations, and Gaussian‐distributed constellations, over Rayleigh fading channel, where

,

, and

.

Figure 5.11 The lower capacity of SMX in the presence of CSE compared to the simulated mutual information of SMX in the presence of CSE over MISO Rayleigh fading channels,

complex Gaussian distributed symbols,

and

.

Figure 5.12 The capacity of SMTs compared to simulated mutual information of SSK over Rayleigh fading channel in the presence of CSE for

, and

, and

.

Figure 5.13 The capacity of SMT compared to the simulated mutual information of SM using PSK constellations, and Gaussian‐distributed constellations, over Rayleigh fading channel in the presence of CSE, where

,

, and

.

Figure 5.14 The capacity of SMT compared to the simulated mutual information of SM, QSM, and SMX over Rayleigh fading channels for different spectral efficiency, where

and

and

.

Figure 5.15 The capacity of SMT compared to the simulated mutual information of SM, QSM, and SMX over Nakagami‐

fading channels for different spectral efficiencies, where

and

and

.

Figure 5.16 The capacity of SMT compared to the simulated mutual information of SM, QSM, and SMX over Rayleigh fading channels in the presence of CSE with

for different spectral efficiencies, where

and

and

.

Chapter 06

Figure 6.1 AF cooperative SMT system model. A system with

transmit antennas at the source,

receive antennas at the destination, and with

AF relays are considered.

Figure 6.2 Simulation, analytical, and asymptotic results for AF SSK system with

,

, and variable

.

Figure 6.3 Analytical, simulation, and asymptotic results for an AF QSM system with

,

, and 4‐QAM modulation while varying

.

Figure 6.4 AF cooperative QSM and SM performance comparison with

 bits and with 4‐QAM modulation assuming

for QSM and

for SM and

and

.

Figure 6.5 AF cooperative QSM and SM performance comparison with

 bits and with 4‐QAM modulation assuming

for QSM and

for SM and

.

Figure 6.6 DF cooperative SMTs system model. A system with

transmit antennas at the source,

receive antennas at the destination, and with

transmit and

receive antennas at the DF relays are considered.

Figure 6.7 Simulation, analytical and asymptotic results for cooperative DF SM system with

,

, BPSK modulation and variable

.

Figure 6.8 Simulation, analytical, and asymptotic results for cooperative DF SM system with

,

, and QPSK modulation and variable

.

Figure 6.9 A two‐way relaying system model applying SMTs at any transmitting node. It is assumed that the sources

and

are equipped, respectively, with

and

transmit antennas and

and

receive antennas, and the relay has

transmit and

receive antennas.

Figure 6.10 Simulation and analytical results for the ABER versus the average SNR for 2WR QSM MIMO system. The block length is

bits per channel use for each transmitting node. The nodes are assumed to have two transmit antennas and using 4‐QAM modulation order. The number of received antennas is varied from

.

Figure 6.11 Simulation and analytical results for the ABER versus the average SNR for 2WR QSM MIMO system. The block length is

bits per channel use from each transmitting node. Each node is assumed to be equipped with two transmit antennas and transmits an 4‐QAM symbol. The number of received antennas is varied from

.

Chapter 07

Figure 7.1 Simulated mutual information comparison for mmWave‐SM over OSA and RSA channels, with

 GHz,

 m,

,

and

, and

.

Figure 7.2 ABER performance comparison for mmWave‐QSM over OSA and RSA channels, with

 GHz,

 m,

 bits,

, and

, and

.

Figure 7.3 Simulated mutual information comparison between SM, QSM, and SMX over LOS mmWave channel using OSA, with

 GHz,

 m,

Space Modulation Techniques

Raed Mesleh

German Jordanian University, Amman, Jordan

Abdelhamid Alhassi

University of Benghazi, Benghazi, Libya

Copyright

This edition first published 2018

© 2018 John Wiley & Sons, Inc.

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 Raed Mesleh, Abdelhamid Alhassi to be identified as the authors of this work has been asserted in accordance with law.

Registered Offices

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

Editorial Office

111 River Street, Hoboken, NJ 07030, USA

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Wiley also publishes its books in a variety of electronic formats and by print-on-demand. Some content that appears in standard print versions of this book may not be available in other formats.

Limit of Liability/Disclaimer of Warranty

In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. 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: Mesleh, Raed, 1978- author. | Alhassi, Abdelhamid, 1986- author.

Title: Space modulation techniques / by Raed Mesleh, Abdelhamid Alhassi.

Description: 1st edition. | Hoboken, NJ : John Wiley & Sons, 2018. |

Identifiers: LCCN 2018000551 (print) | LCCN 2018007146 (ebook) | ISBN 9781119375678

(pdf) | ISBN 9781119375685 (epub) | ISBN 9781119375654 (cloth)

Subjects: LCSH: Amplitude modulation. | Wireless communication systems--Technological

innovations.

Classification: LCC TK6553 (ebook) | LCC TK6553 .M474 2018 (print) | DDC 621.382--dc23

LC record available at https://lccn.loc.gov/2018000551

Cover design: Wiley

Cover image: © StationaryTraveller/iStockphoto

Dedication

To my mother and wife for their unending love and support and to my father who could not see this book completed.

Raed Mesleh

To my parents, Houssein and Mareia, and my wife Farah, for their care, love, and support.

Abdelhamid Alhassi

Preface

The inspiration for this book arose from the desire to enlighten and instill a greater appreciation among wireless engineering society about a very promising technology for future wireless systems. Through this treatise, we aspire to expound the several benefits of space modulation techniques (SMTs) and demonstrate the several opportunities they convey. We believe that this book is also a unique tribute to the many scientists who were involved in the development of SMTs in the past 10 years.

SMT technology has come about from research that began 10 years ago and formed a basis for the work to be applied in what were then termed “beyond 4G” or B4G technologies before any consideration of what will be adopted within 5G networks. The attractiveness of the technology is that it enables the possibility to achieve comparable data throughput to a similar MIMO system yet with as few as just one radio transceiver at each end. Otherwise, in conventional MIMO, several transceivers would be required ranging anything from 4 to 128 in next generation communication systems, which would be costly and energy inefficient. Therefore, SMTs are now reaching a matured level that they are integrated in this book to assist the research and development community in learning about the concepts. The book identifies and discusses in detail a number of emerging techniques for high data rate wireless communication systems. The book serves also as a motivating source for further research and development activities in SMT. The limitations of current approaches and challenges of emerging concepts are discussed. Furthermore, new directions of research and development are identified, hopefully providing fresh ideas and influential research topics to the interested readers.

SMTs provide unique method to convey information bits and require innovative thinking, which goes beyond existing theories. The book provides a comprehensive overview on the basic working principle of coherent and noncoherent SMTs. Practical system models with the minimum number of needed RF‐chains at the transmitter are presented and discussed in terms of hardware cost, power efficiency, performance, and computational complexity. The advantages and disadvantages of each technique along with their detailed performance are discoursed. A general framework for analyzing the performance of these techniques is provided and used to provide detailed performance analysis over several generalized fading channels. In addition, capacity analysis of SMTs is provided and thoroughly discussed.

Raed Mesleh

Abdelhamid Alhassi

Amman, Jordan

Benghazi, Libya, November 2017

Chapter 1Introduction

1.1 Wireless History

Wireless technology revolution started in 1896 when Guglielmo Marconi demonstrated a transmission of a signal through free space without placing a physical medium between the transmitter and the receiver [1, 2]. Based on the success of that experiment, several wireless applications were developed. Yet, it was widely believed that reliable communication over a noisy channel can be only achieved through either reducing data rate or increasing the transmitted signal power. In 1948, Claude Shannon characterizes the limits of reliable communication and showed that this belief is incorrect [3]. Alternatively, he demonstrated that through an intelligent coding of the information, communication at a strictly positive rate with small error probability can be achieved. There is, however, a maximal rate, called the channel capacity, for which this can be done. If communication is attempted beyond that rate, it is infeasible to drive the error probability to zero [4].

Since then, wireless technologies have experienced a preternatural growth. There are many systems in which wireless communication is applicable. Radio and television broadcasting along with satellite communication are perhaps some of the earliest successful common applications. However, the recent interest in wireless communication is perhaps inspired mostly by the establishment of the first‐generation (1G) cellular phones in the early 1980s [5–7]. 1G wireless systems consider analog transmission and support voice services only. Second‐generation (2G) cellular networks, introduced in the early 1990s, upgrade to digital technologies and cover services such as facsimile and low data rate (up to 9.6 kbps) in addition to voice [8, 9]. The enhanced versions of the second–generation (2G) systems, sometimes referred to as 2.5G systems, support more advanced services like medium‐rate (up to 100 kbps) circuit‐ and packet‐switched data [10–12]. Third‐generation (3G) mobile systems were standardized around year 2000 to support high bit rate (144–384) kbps for fast‐moving users and up to 2.048 Mbps for slow‐moving users [13–15]. Following the third–generation (3G) concept, several enhanced technologies generally called 3.5G, such as high speed downlink packet access (HSPDA), which increases the downlink data rate up to 3.6 Mbps were proposed [16, 17]. Regardless of the huge developments in data rate from 1G to 3G and beyond systems, the demand for more data rate did not seem to layover at any point in near future. As such, much more enhanced techniques were developed leading to fourth‐generation (4G) wireless standard. 4G systems promise data rates in the range of 1 Gbps and witnessed significant development and research interest since launched in 2013 [18]. However, a recent CISCO forecast [19] reported that global mobile data traffic grew 74% in 2015, where it reached 3.7 EB per month at the end of 2015, up from 2.1 EB per month at the end of 2014. As well, it is reported that mobile data traffic has grown 4000‐fold over the past 10 years and almost 400‐million‐fold over the past 15 years. It is also anticipated in the same forecast that mobile data traffic will reach 30.6 EB by 2020, and the number of mobile‐connected devices per capita will reach 1.5 [19]. With such huge demand for more data rates and better quality services, fifth‐generation (5G) wireless standard is anticipated to be launched in 2020 and has been under intensive investigations in the past few years [20]. 5G standard is supposed to provide a downlink peak date rate of 20 Gbps and peak spectral efficiency of 30 b (s/Hz)−1 [20]. Such huge data rate necessitates the need of new spectrum and more energy‐efficient physical layer techniques [21].

1.2 MIMO Promise

Physical layer techniques such as millimeter‐wave (mmWave) communications, cognitive and cooperative communications, visible light and free‐space optical communications, and multiple‐input multiple‐output (MIMO) and massive MIMO techniques are under extensive investigations at the moment for possible deployments in 5G networks [21]. Among the set of existing technologies, MIMO systems promise a boost in the spectral efficiency by simultaneously transmitting data from multiple transmit antennas to the receiver [22–28].

In 1987, Jack Winters inspired by the work of Salz [23], investigated the fundamental limits on systems that exploit multipath propagation to allow multiple simultaneous transmission in the same bandwidth [29]. Later in 1991, Wittneben proposed the first bandwidth‐efficient transmit diversity scheme in [30], where it was revealed that the diversity advantage of the proposed scheme is equal to the number of transmit antennas which is optimal [31]. Alamouti discovered a new and simple transmit diversity technique [24] that is generalized later by Tarokh et al. and given the name of space–time coding (STC) [32]. STC techniques achieve diversity gains by transmitting multiple, redundant copies of a data stream to the receiver in order to allow reliable decoding. Shortly after, Foschini introduced multilayered space–time architecture, called Bell Labs layered space time (BLAST), that uses spatial multiplexing to increase the data rate and not necessarily provides transmit diversity [27]. Capacity analysis of MIMO systems was reported by Telatar and shown that MIMO capacity increases linearly with the minimum number among the transmit and receiver antennas [25] as compared to a system with single transmit and receive antennas. However, spatial multiplexing (SMX) MIMO systems, as BLAST, suffer from several limitations that hinder their practical implementations. Simultaneous transmission of independent data from multiple transmit antennas creates high inter‐channel interference (ICI) at the receiver input, which requires high computational complexity to be resolved. In addition, the presence of high ICI degrades the performance of SMX MIMO systems, significant performance degradations are reported for any channel imperfections [33, 34]. On the other hand, STC techniques alleviate SMX challenges at the cost of achievable data rate. In STCs, the maximum achievable spectral efficiency is one symbol per channel use and can be achieved only with two transmit antennas.

1.3 Introducing Space Modulation Techniques (SMTs)

Another group of MIMO techniques, called space modulation techniques (SMTs), consider an innovative approach to tackle previous challenges of MIMO systems. In SMTs, a new spatial constellation diagram is added and utilized to enhance the spectral efficiency while conserving energy resources and receiver computational complexity. The basic idea stems from [35] where a binary phase shift keying (BPSK) symbol is used to indicate an active antenna among the set of existing multiple antennas. The receiver estimates the transmitted BPSK symbol and the antenna that transmits this symbol. However, the first popular SMT was proposed by Mesleh et al. [36, 37