Space Modulation Techniques

Standards Information Network (Verlag)
  • 1. Auflage
  • |
  • erschienen am 11. Mai 2018
  • |
  • 288 Seiten
E-Book | ePUB mit Adobe-DRM | Systemvoraussetzungen
978-1-119-37568-5 (ISBN)
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.
1. Auflage
  • Englisch
  • USA
John Wiley & Sons Inc
  • Für Beruf und Forschung
  • 26,51 MB
978-1-119-37568-5 (9781119375685)

weitere Ausgaben werden ermittelt
Raed Mesleh, PhD, works in the Electrical and Communications Engineering Department, School of Electrical Engineering and Information Technology, German Jordanian University in Amman, Jordan.

Abdelhamid Alhassi, PhD, works in the Department of Electrical and Electronics Engineering, Faculty of Engineering, the University of Benghazi, Benghazi, Libya.
Preface xiii

1 Introduction 1

1.1 Wireless History 1

1.2 MIMO Promise 2

1.3 Introducing Space Modulation Techniques (SMTs) 3

1.4 Advanced SMTs 4

1.4.1 Space-Time Shift Keying (STSK) 4

1.4.2 Index Modulation (IM) 4

1.4.3 Differential SMTs 5

1.4.4 OpticalWireless SMTs 6

1.5 Book Organization 6

2 MIMO System and ChannelModels 9

2.1 MIMO System Model 9

2.2 SpatialMultiplexing MIMO Systems 11

2.3 MIMO Capacity 11

2.4 MIMO ChannelModels 13

2.4.1 Rayleigh Fading 15

2.4.2 Nakagami-n (Rician Fading) 15

2.4.3 Nakagami-m Fading 16

2.4.4 The ;;-;; MIMO Channel 17

2.4.5 The ;;-;; Distribution 20

2.4.6 The ;;-;; Distribution 23

2.5 Channel Imperfections 26

2.5.1 Spatial Correlation 26 Simulating SC Matrix 29 Effect of SC on MIMO Capacity 31

2.5.2 Mutual Coupling 31 Effect of MC on MIMO Capacity 33

2.5.3 Channel Estimation Errors 34 Impact of Channel Estimation Error on the MIMO Capacity 34

3 SpaceModulation Transmission and Reception Techniques 35

3.1 Space Shift Keying (SSK) 36

3.2 Generalized Space Shift Keying (GSSK) 39

3.3 SpatialModulation (SM) 41

3.4 Generalized SpatialModulation (GSM) 44

3.5 Quadrature Space Shift Keying (QSSK) 45

3.6 Quadrature SpatialModulation (QSM) 48

3.7 Generalized QSSK (GQSSK) 53

3.8 Generalized QSM (GQSM) 55

3.9 Advanced SMTs 55

3.9.1 Differential Space Shift Keying (DSSK) 55

3.9.2 Differential SpatialModulation (DSM) 60

3.9.3 Differential Quadrature SpatialModulation (DQSM) 60

3.9.4 Space-Time Shift Keying (STSK) 65

3.9.5 Trellis Coded-Spatial Modulation (TCSM) 66

3.10 Complexity Analysis of SMTs 69

3.10.1 Computational Complexity of the ML Decoder 69

3.10.2 Low-Complexity Sphere Decoder Receiver for SMTs 70 SMT-Rx Detector 70 SMT-Tx Detector 71 Single Spatial Symbol SMTs (SS-SMTs) 71 Double Spatial Symbols SMTs (DS-SMTs) 72 Computational Complexity 73 Error Probability Analysis and Initial Radius 74

3.11 Transmitter Power Consumption Analysis 75

3.11.1 Power Consumption Comparison 77

3.12 Hardware Cost 80

3.12.1 Hardware Cost Comparison 81

3.13 SMTs Coherent and Noncoherent Spectral Efficiencies 82

4 Average Bit Error Probability Analysis for SMTs 85

4.1 Average Error Probability over Rayleigh Fading Channels 85

4.1.1 SM and SSK with Perfect Channel Knowledge at the Receiver 85 Single Receive Antenna (Nr = 1) 86 Arbitrary Number of Receive Antennas (Nr) 88 Asymptotic Analysis 89

4.1.2 SM and SSK in the Presence of Imperfect Channel Estimation 90 Single Receive Antenna (Nr = 1) 91 Arbitrary Number of Receive Antennas (Nr) 92 Asymptotic Analysis 92

4.1.3 QSM with Perfect Channel Knowledge at the Receiver 94

4.1.4 QSM in the Presence of Imperfect Channel Estimation 96

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 98

4.3 Average Error Probability Analysis of Differential SMTs 101

4.4 Comparative Average Bit Error Rate Results 103

4.4.1 SMTs, GSMTs, and QSMTs ABER Comparisons 103

4.4.2 Differential SMTs Results 107

5 Information Theoretic Treatment for SMTs 109

5.1 Evaluating the Mutual Information 110

5.1.1 Classical SpatialMultiplexing MIMO 110

5.1.2 SMTs 111

5.2 Capacity Analysis 114

5.2.1 SMX 114

5.2.2 SMTs 115 Classical SMTs Capacity Analysis 115 SMTs Capacity Analysis by Maximing over Spatial and Constellation Symbols 119

5.3 Achieving SMTs Capacity 121

5.3.1 SSK 121

5.3.2 SM 124

5.4 Information Theoretic Analysis in the Presence of Channel Estimation Errors 128

5.4.1 Evaluating the Mutual Information 128 Classical SpatialMultiplexing MIMO 128 SMTs 129

5.4.2 Capacity Analysis 131 SpatialMultiplexing MIMO 131 SMTs 134

5.4.3 Achieving SMTs Capacity 135 SSK 135 SM 136

5.5 Mutual Information Performance Comparison 138

6 Cooperative SMTs 141

6.1 Amplify and Forward (AF) Relaying 141

6.1.1 Average Error Probability Analysis 143 Asymptotic Analysis 147 Numerical Results 147

6.1.2 Opportunistic AF Relaying 149 Average Error Probability Analysis 151 Asymptotic Analysis 152

6.2 Decode and Forward (DF) Relaying 152

6.2.1 Multiple single-antenna DF relays 152

6.2.2 Single DF Relay with Multiple Antennas 153

6.2.3 Average Error Potability Analysis 154 Multiple Single-Antenna DF Relays 154 Single DF Relay with Multiple-Antennas 157 Numerical Results 157

6.3 Two-Way Relaying (2WR) SMTs 158

6.3.1 The Transmission Phase 159

6.3.2 The Relaying Phase 161

6.3.3 Average Error Probability Analysis 162 Numerical Results 165

7 SMTs for Millimeter-Wave Communications 167

7.1 Line of Sight mmWave Channel Model 168

7.1.1 Capacity Analysis 168 SM 168 QSM 169 Randomly Spaced Antennas 169 Capacity Performance Comparison 172

7.1.2 Average Bit Error Rate Results 174

7.2 Outdoor Millimeter-Wave Communications 3D Channel Model 175

7.2.1 Capacity Analysis 179

7.2.2 Average Bit Error Rate Results 182

8 Summary and Future Directions 185

8.1 Summary 185

8.2 Future Directions 187

8.2.1 SMTs with Reconfigurable Antennas (RAs) 187

8.2.2 Practical Implementation of SMTs 188

8.2.3 Index Modulation and SMTs 188

8.2.4 SMTs for OpticalWireless Communications 189

A MatlabCodes 191

A.1 Generating the Constellation Diagrams 191

A.1.1 SSK 191

A.1.2 GSSK 192

A.1.3 SM 193

A.1.4 GSM 194

A.1.5 QSSK 195

A.1.6 QSM 196

A.1.7 GQSSK 197

A.1.8 GQSM 199

A.1.9 SMTs 200

A.1.10 DSSK 202

A.1.11 DSM 203

A.1.12 DSMTs 204

A.2 Receivers 205

A.2.1 SMTs ML Receiver 205

A.2.2 DSMTs ML Receiver 206

A.3 Analytical and Simulated ABER 207

A.3.1 ABER of SM over Rayleigh Fading Channels with No CSE 207

A.3.2 ABER of SM over Rayleigh Fading Channels with CSE 209

A.3.3 ABER of QSM over Rayleigh Fading Channels with No CSE 211

A.3.4 ABER of QSM over Rayleigh Fading Channels with CSE 214

A.3.5 Analytical ABER of SMTs over Generalized Fading Channels and with CSE and SC 216

A.3.6 Simulated ABER of SMTs Using Monte Carlo Simulation over Generalized Fading Channels and with CSE and SC 222

A.3.7 Analytical ABER of DSMTs over Generalized Fading Channels 228

A.3.8 Simulated ABER of DSMTs Using Monte Carlo Simulation over Generalized Fading Channels 232

A.4 Mutual Information and Capacity 235

A.4.1 SMTs Simulated Mutual Information over Generalized Fading Channels and with CSE 235

A.4.2 SMTs Capacity 240

References 243

Index 265

Chapter 1

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] and called spatial modulation (SM), and all other SMTs are driven as spacial or generalized cases from SM. Opposite to traditional modulation schemes, SM conveys information by utilizing the multipath nature of the MIMO fading channel as an extra constellation diagram referred to as spatial constellation. The incoming data bits modulate the spatial constellation symbol, which represents the spatial position, or index, of one of the available transmit antennas that will be activated at this particular time to transmit a modulated carrier signal by a complex symbol drawn from an arbitrary constellation diagram. SM was the first scheme to define the concept of spatial constellation and proposes the use of modulating spatial symbols to convey information. It was shown that SM can achieve multiplexing gain while maintaining free ICI [37], reduced receiver computational complexity [38], enhances the bit error probability [39], and promises the use of single radio frequency (RF)-chain transmitter [40]. As such, the concept of SM attracted significant research interests, and different performance aspects were studied thoroughly in few years [41-88]. Hence, multiple variant schemes applying similar SM concept were proposed. In [89], space shift keying (SSK) system was proposed where only spatial symbols exist and no data symbol is transmitted. Generalized spatial modulation (GSM) where more than one transmit antenna is activated at each time instant to transmit identical data is proposed in [67]. Similarly, generalized space shift keying (GSSK) was proposed in [69]. In all these schemes, single-dimensional spatial constellation diagram was created and used to convey spatial bits. In [65, 70], an additional quadrature spatial constellation diagram is defined where the real part of the complex data symbol is transmitted from one spatial symbol and the imaginary part of the complex symbol is transmitted from another spatial symbol. As such, data rate enhancement of base two logarithm of the number of transmit antennas is achieved while maintaining all previous SM advantages. These schemes are called quadrature spatial modulation (QSM) and quadrature space shift keying (QSSK). In addition,...

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