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E-grāmata: MIMO-OFDM for LTE, WiFi and WiMAX: Coherent versus Non-coherent and Cooperative Turbo Transceivers

(New Postcom Equipment Co. Ltd., China), (University of Southampton, UK), (University of Southampton), (The University of the West Indies)
  • Formāts: PDF+DRM
  • Sērija : IEEE Press
  • Izdošanas datums: 03-Sep-2010
  • Izdevniecība: Wiley-IEEE Press
  • Valoda: eng
  • ISBN-13: 9780470711767
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MIMO-OFDM for LTE, WiFi and WiMAX: Coherent versus Non-coherent and Cooperative Turbo TransceiversPalielināt
  • Formāts: PDF+DRM
  • Sērija : IEEE Press
  • Izdošanas datums: 03-Sep-2010
  • Izdevniecība: Wiley-IEEE Press
  • Valoda: eng
  • ISBN-13: 9780470711767
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This book provides a comprehensive and up-to-date portrayal of wireless transmission based on OFDM techniques augmented with Space-Time Block Codes (STBCs) and Spatial-Division Multiple Access (SDMA). The volume also offers an in-depth treatment of cutting-edge cooperative communications. Topics include channel coding and multiuser detection (MUD); the benefits of turbo and LDPC channel coding; the entire suite of known joint coding and modulation schemes; and space-time coding as well as SDM/SDMA MIMOs within the context of various application examples. The book also includes an extensive bibliography, as well as both subject and author indexes. Annotation ©2011 Book News, Inc., Portland, OR (booknews.com)

MIMO-OFDM for LTE, WIFI and WIMAX: Coherent versus Non-Coherent and Cooperative Turbo-Transceivers provides an up-to-date portrayal of wireless transmission based on OFDM techniques augmented with Space-Time Block Codes (STBCs) and Spatial-Division Multiple Access (SDMA). The volume also offers an in-depth treatment of cutting-edge Cooperative Communications.

This monograph collates the latest techniques in a number of specific design areas of turbo-detected MIMO-OFDM wireless systems. As a result a wide range of topical subjects are examined, including channel coding and multiuser detection (MUD), with a special emphasis on optimum maximum-likelihood (ML) MUDs, reduced-complexity genetic algorithm aided near-ML MUDs and sphere detection. The benefits of spreading codes as well as joint iterative channel and data estimation are only a few of the radical new features of the book.

Also considered are the benefits of turbo and LDPC channel coding, the entire suite of known joint coding and modulation schemes, space-time coding as well as SDM/SDMA MIMOs within the context of various application examples. The book systematically converts the lessons of Shannon's information theory into design principles applicable to practical wireless systems; the depth of discussions increases towards the end of the book.

  • Discusses many state-of-the-art topics important to today's wireless communications engineers.
  • Includes numerous complete system design examples for the industrial practitioner.
  • Offers a detailed portrayal of sphere detection.
  • Based on over twenty years of research into OFDM in the context of various applications, subsequently presenting comprehensive bibliographies.

Recenzijas

"This book provides a comprehensive and up-to-date portrayal of wireless transmission based on OFDM techniques augmented with Space-Time Block Codes (STBCs) and Spatial-Division Multiple Access (SDMA) . . . the book also includes an extensive bibliography, as well as both subject and author indexes. (Annotation ©2011 Book News Inc. Portland, OR)." (Booknews, 1 April 2011) "The book systematically converts the lessons of Shannon's information theory into design principles applicable to practical wireless systems; the depth of discussions increases towards the end of the book.". (4G Wireless Evolution , 26 January 2011)

About the Authors xix
Other Wiley–IEEE Press Books on Related Topics xxi
Preface xxiii
Acknowledgments xxvii
List of Symbols xxix
1 Introduction to OFDM and MIMO-OFDM
1(36)
1.1 OFDM History
1(8)
1.1.1 MIMO-Assisted OFDM
2(20)
1.1.1.1 The Benefits of MIMOs
2(3)
1.1.1.2 MIMO-OFDM
5(1)
1.1.1.3 SDMA-based MIMO-OFDM Systems
6(3)
1.2 OFDM Schematic
9(3)
1.3 Channel Estimation for Multi-carrier Systems
12(3)
1.4 Channel Estimation for MIMO-OFDM
15(1)
1.5 Signal Detection in MIMO-OFDM Systems
16(5)
1.6 Iterative Signal Processing for SDM-OFDM
21(1)
1.7 System Model
22(7)
1.7.1 Channel Statistics
22(3)
1.7.2 Realistic Channel Properties
25(1)
1.7.3 Baseline Scenario Characteristics
26(1)
1.7.4 MC Transceiver
27(2)
1.8 SDM-OFDM System Model
29(4)
1.8.1 MIMO Channel Model
29(1)
1.8.2 Channel Capacity
30(1)
1.8.3 SDM-OFDM Transceiver Structure
31(2)
1.9 Novel Aspects and Outline of the Book
33(3)
1.10
Chapter Summary
36(1)
2 OFDM Standards
37(24)
2.1 Wi-Fi
37(1)
2.1.1 IEEE 802.11 Standards
38(1)
2.2 3GPP LTE
38(1)
2.3 WiMAX Evolution
39(20)
2.3.1 Historic Background
41(6)
2.3.1.1 IEEE 802.16 Standard Family
41(1)
2.3.1.2 Early 802.16 Standards
41(1)
2.3.1.2.1 IEEE 802.16d-2004 – Fixed WiMAX
43(1)
2.3.1.2.2 IEEE 802.16e-2005 – Mobile WiMAX
43(1)
2.3.1.2.3 Other 802.16 Standards
45(1)
2.3.1.3 WiMAX Forum
46(1)
2.3.1.4 WiMAX and WiBro
47(1)
2.3.2 Technical Aspects of WiMAX
47(11)
2.3.2.1 WiMAX-I: 802.16-2004 and 802.16e-2005
48(1)
2.3.2.1.1 OFDMA System Configuration
48(1)
2.3.2.1.2 Frame Structure
48(1)
2.3.2.1.3 Subcarrier Mapping
49(1)
2.3.2.1.4 Channel Coding
50(1)
2.3.2.1.5 MIMO Support
50(1)
2.3.2.1.6 Other Aspects
52(1)
2.3.2.2 WiMAX-II: 802.16m
52(1)
2.3.2.2.1 System Requirements
52(1)
2.3.2.2.2 System Description
54(4)
2.3.3 The Future of WiMAX
58(1)
2.4
Chapter Summary
59(2)
Part I Coherently Detected SDMA-OFDM Systems 61(210)
3 Channel Coding Assisted STBC-OFDM Systems
63(46)
3.1 Introduction
63(1)
3.2 Space–Time Block Codes
63(12)
3.2.1 Alamouti's G2 STBC
64(2)
3.2.2 Encoding Algorithm
66(1)
3.2.2.1 Transmission Matrix
66(1)
3.2.2.2 Encoding Algorithm of the STBC G2
66(1)
3.2.2.3 Other STBCs
66(1)
3.2.3 Decoding Algorithm
67(3)
3.2.3.1 Maximum Likelihood Decoding
67(1)
3.2.3.2 Maximum-A-Posteriori Decoding
68(2)
3.2.4 System Overview
70(1)
3.2.5 Simulation Results
70(5)
3.2.5.1 Performance over Uncorrelated Rayleigh Fading Channels
71(2)
3.2.5.2 Performance over Correlated Rayleigh Fading Channel
73(2)
3.2.6 Conclusions
75(1)
3.3 Channel-Coded STBCs
75(20)
3.3.1 STBCs with LDPC Channel Codes
76(14)
3.3.1.1 System Overview
77(1)
3.3.1.2 Simulation Results
78(1)
3.3.1.2.1 Performance over Uncorrelated Rayleigh Fading Channels
79(1)
3.3.1.2.2 Performance over Correlated Rayleigh Fading Channels
82(4)
3.3.1.3 Complexity Issues
86(4)
3.3.1.4 Conclusions
90(1)
3.3.2 LDPC-Aided and TC-Aided STBCs
90(5)
3.3.2.1 System Overview
91(1)
3.3.2.2 Complexity Issues
91(1)
3.3.2.3 Simulation Results
92(1)
3.3.2.4 Conclusions
93(2)
3.4 Channel Coding Aided STBC-OFDM
95(11)
3.4.1 CM-Assisted STBCs
95(8)
3.4.1.1 CM Principles
96(1)
3.4.1.2 Inter-symbol Interference and OFDM Basics
96(1)
3.4.1.3 System Overview
97(1)
3.4.1.3.1 Complexity Issues
98(1)
3.4.1.3.2 Channel Model
98(1)
3.4.1.3.3 Assumptions
98(2)
3.4.1.4 Simulation Results
100(2)
3.4.1.5 Conclusions
102(1)
3.4.2 CM-Aided and LDPC-Aided STBC-OFDM Schemes
103(7)
3.4.2.1 System Overview
104(1)
3.4.2.2 Simulation Results
105(1)
3.4.2.3 Conclusions
106(1)
3.5
Chapter Summary
106(3)
4 Coded Modulation Assisted Multi-user SDMA-OFDM Using Frequency-Domain Spreading
109(30)
4.1 Introduction
109(1)
4.2 System Model
110(3)
4.2.1 SDMA MIMO Channel Model
110(1)
4.2.2 CM-Assisted SDMA-OFDM Using Frequency-Domain Spreading
111(2)
4.2.2.1 MMSE MUD
111(1)
4.2.2.2 Subcarrier-Based WHTS
112(1)
4.3 Simulation Results
113(26)
4.3.1 MMSE-SDMA-OFDM Using WHTS
114(1)
4.3.2 CM- and WHTS-assisted MMSE-SDMA-OFDM
115(20)
4.3.2.1 Performance over the SWATM Channel
115(1)
4.3.2.1.1 Two Receiver Antenna Elements
116(1)
4.3.2.1.2 Four Receiver Antenna Elements
119(1)
4.3.2.2 Performance over the COST207 HT Channel
119(1)
4.3.2.2.1 Two Receiver Antenna Elements
120(1)
4.3.2.2.2 Four Receiver Antenna Elements
126(1)
4.3.2.2.3 Performance Comparisons
127(5)
4.3.2.3 Effects of the WHT Block Size
132(1)
4.3.2.4 Effects of the Doppler Frequency
133(2)
4.4
Chapter Summary
135(4)
5 Hybrid Multi-user Detection for SDMA-OFDM Systems
139(32)
5.1 Introduction
139(1)
5.2 GA-Assisted MUD
140(8)
5.2.1 System Overview
140(1)
5.2.2 MMSE-GA-concatenated MUD
141(3)
5.2.2.1 Optimization Metric for the GA MUD
141(1)
5.2.2.2 Concatenated MMSE-GA MUD
142(2)
5.2.3 Simulation Results
144(2)
5.2.4 Complexity Analysis
146(1)
5.2.5 Conclusions
147(1)
5.3 Enhanced GA-based MUD
148(20)
5.3.1 Improved Mutation Scheme
148(7)
5.3.1.1 Conventional Uniform Mutation
148(1)
5.3.1.2 Biased Q-function-Based Mutation
149(1)
5.3.1.2.1 Theoretical Foundations
150(1)
5.3.1.2.2 Simplified BQM
152(1)
5.3.1.3 Simulation Results
153(1)
5.3.1.3.1 BQM Versus UM
153(1)
5.3.1.3.2 BQM Versus CNUM
155(1)
5.3.2 Iterative MUD Framework
155(10)
5.3.2.1 MMSE-Initialized Iterative GA MUD
155(1)
5.3.2.2 Simulation Results
156(1)
5.3.2.2.1 Performance in Underloaded and Fully Loaded Scenarios
158(1)
5.3.2.2.1.1 BQM-IGA Performance
159(1)
5.3.2.2.1.2 Effects of the Number of 1GA MUD Iterations
160(1)
5.3.2.2.1.3 Effects of the User Load
161(1)
5.3.2.2.2 Performance in Overloaded Scenarios
161(1)
5.3.2.2.2.1 Overloaded BQM-IGA
162(1)
5.3.2.2.2.2 BQM Versus CNUM
164(1)
5.3.2.2.3 Performance under Imperfect Channel Estimation
164(1)
5.3.3 Complexity Analysis
165(3)
5.3.4 Conclusions
168(1)
5.4
Chapter Summary
168(3)
6 Direct-Sequence Spreading and Slow Subcarrier-Hopping Aided Multi-user SDMA-OFDM Systems
171(30)
6.1 Conventional SDMA-OFDM Systems
171(1)
6.2 Introduction to Hybrid SDMA-OFDM
172(1)
6.3 Subband Hopping Versus Subcarrier Hopping
173(2)
6.4 System Architecture
175(13)
6.4.1 System Overview
175(3)
6.4.1.1 Transmitter Structure
176(2)
6.4.1.2 Receiver Structure
178(1)
6.4.2 Subcarrier-Hopping Strategy Design
178(6)
6.4.2.1 Random SSCH
180(1)
6.4.2.2 Uniform SSCH
180(1)
6.4.2.2.1 Design of the USSCH Pattern
180(1)
6.4.2.2.2 Discussions
183(1)
6.4.2.3 Random and Uniform SFH
184(1)
6.4.2.4 Offline Pattern Pre-computation
184(1)
6.4.3 DSS Despreading and SSCH Demapping
184(2)
6.4.4 MUD
186(2)
6.5 Simulation Results
188(8)
6.5.1 MMSE-Aided Versus MMSE-IGA-Aided DSS/SSCH SDMA-OFDM
190(1)
6.5.2 SDMA-OFDM Using SFH and Hybrid DSS/SSCH Techniques
191(3)
6.5.2.1 Moderately Overloaded Scenarios
191(1)
6.5.2.2 Highly Overloaded Scenarios
192(2)
6.5.3 Performance Enhancements by Increasing Receiver Diversity
194(2)
6.5.4 Performance under Imperfect Channel Estimation
196(1)
6.6 Complexity Issues
196(1)
6.7 Conclusions
197(1)
6.8
Chapter Summary
197(4)
7 Channel Estimation for OFDM and MC-CDMA
201(46)
7.1 Pilot-Assisted Channel Estimation
201(1)
7.2 Decision-Directed Channel Estimation
202(1)
7.3 A Posteriori FD-CTF Estimation
203(3)
7.3.1 Least-Squares CTF Estimator
203(1)
7.3.2 MMSE CTF Estimator
204(2)
7.3.3 A Priori Predicted-Value-Aided CTF Estimator
206(1)
7.4 A Posteriori CIR Estimation
206(10)
7.4.1 MMSE SS-CIR Estimator
206(1)
7.4.2 Reduced-Complexity SS-CIR Estimator
207(3)
7.4.3 Complexity Study
210(1)
7.4.4 MMSE FS-CIR Estimator
210(1)
7.4.5 Performance Analysis
211(5)
7.4.5.1 RC-MMSE SS-CIR Estimator Performance
213(1)
7.4.5.2 Fractionally Spaced CIR Estimator Performance
214(2)
7.5 Parametric FS-CIR Estimation
216(7)
7.5.1 Projection Approximation Subspace Tracking
216(4)
7.5.2 Deflation PAST
220(1)
7.5.3 PASTD-Aided FS-CIR Estimation
220(3)
7.6 Time-Domain A Priori CM Tap Prediction
223(7)
7.6.1 MMSE Predictor
224(1)
7.6.2 Robust Predictor
225(1)
7.6.3 MMSE Versus Robust Predictor Performance Comparison
226(1)
7.6.4 Adaptive RLS Predictor
227(2)
7.6.5 Robust Versus Adaptive Predictor Performance Comparison
229(1)
7.7 PASTD-Aided DDCE
230(3)
7.8 Channel Estimation for MIMO-OFDM
233(12)
7.8.1 Soft Recursive MIMO-CTF Estimation
233(7)
7.8.1.1 LMS MIMO-CTF Estimator
233(3)
7.8.1.2 RLS MIMO-CTF Estimator
236(1)
7.8.1.3 Soft-Feedback-Aided RLS MIMO-CTF Estimator
236(1)
7.8.1.4 Modified RLS MIMO-CIF Estimator
237(1)
7.8.1.5 MIMO-CTF Estimator Performance Analysis
238(2)
7.8.2 PASTD-Aided DDCE for MIMO-OFDM
240(10)
7.8.2.1 PASTD-Aided MIMO-DDCE Performance Analysis
240(5)
7.9
Chapter Summary
245(2)
8 Iterative Joint Channel Estimation and MUD for SDMA-OFDM Systems
247(24)
8.1 Introduction
247(2)
8.2 System Overview
249(1)
8.3 GA-Assisted Iterative Joint Channel Estimation and MUD
250(9)
8.3.1 Pilot-Aided Initial Channel Estimation
252(1)
8.3.2 Generating Initial Symbol Estimates
253(2)
8.3.3 GA-Aided Joint Optimization Providing Soft Outputs
255(4)
8.3.3.1 Extended GA Individual Structure
255(1)
8.3.3.2 Initialization
255(1)
8.3.3.3 Joint Genetic Optimization
256(1)
8.3.3.3.1 Cross-over Operator
256(1)
8.3.3.3.2 Mutation Operator
257(1)
8.3.3.3.3 Comments on the Joint Optimization Process
257(1)
8.3.3.4 Generating the GA's Soft Outputs
258(1)
8.4 Simulation Results
259(9)
8.4.1 Effects of the Maximum Mutation Step Size
260(2)
8.4.2 Effects of the Doppler Frequency
262(1)
8.4.3 Effects of the Number of GA-JCEMUD Iterations
263(1)
8.4.4 Effects of the Pilot Overhead
263(1)
8.4.5 Joint Optimization Versus Separate Optimization
263(2)
8.4.6 Comparison of GA-JCEMUDs Having Soft and Hard Outputs
265(1)
8.4.7 MEMO Robustness
265(3)
8.5 Conclusions
268(1)
8.6
Chapter Summary
268(3)
Part II Coherent versus Non-coherent and Cooperative OFDM Systems 271(220)
List of Symbols in Part II
273(2)
9 Reduced-Complexity Sphere Detection for Uncoded SDMA-OFDM Systems
275(32)
9.1 Introduction
275(3)
9.1.1 System Model
275(1)
9.1.2 Maximum Likelihood Detection
276(2)
9.1.3
Chapter Contributions and Outline
278(1)
9.2 Principle of SD
278(11)
9.2.1 Transformation of the ML Metric
278(1)
9.2.2 Depth-First Tree Search
279(4)
9.2.3 Breadth-First Tree Search
283(1)
9.2.4 Generalized Sphere Detection (GSD) for Rank-Deficient Systems
284(1)
9.2.4.1 GSD
284(1)
9.2.4.2 GSD Using a Modified Grammian Matrix
284(1)
9.2.5 Simulation Results
285(4)
9.3 Complexity-Reduction Schemes for SD
289(12)
9.3.1 Complexity-Reduction Schemes for Depth-First SD
289(5)
9.3.1.1 ISR Selection Optimization
289(1)
9.3.1.2 Optimal Detection Ordering
290(1)
9.3.1.3 Search Algorithm Optimization
291(1)
9.3.1.3.1 Sorted Sphere Detection (SSD)
291(1)
9.3.1.3.2 SSD Using Updated Bounds
292(1)
9.3.1.3.3 SSD Using Termination Threshold
293(1)
9.3.2 Complexity-Reduction Schemes for K-Best SD
294(3)
9.3.2.1 Optimal Detection Ordering
294(1)
9.3.2.2 Search-Radius-Aided K-Best SD
295(1)
9.3.2.3 Complexity-Reduction Parameter 6 for Low SNRs
296(1)
9.3.3 OHRSA – An Advanced Extension of SD
297(4)
9.3.3.1 Hierarchical Search Structure
297(2)
9.3.3.2 Optimization Strategies for the OHRSA Versus Complexity-Reduction Techniques for the Depth-First SD
299(1)
9.3.3.2.1 Best-First Detection Strategy
299(1)
9.3.3.2.2 Sorting Criterion
299(1)
9.3.3.2.3 Local Termination Threshold
300(1)
9.3.3.2.4 Performance Evaluation
301(1)
9.4 Comparison of the Depth-First, K-Best and OHRSA Detectors
301(2)
9.4.1 Full-Rank Systems
301(1)
9.4.2 Rank-Deficient Systems
302(1)
9.5
Chapter Conclusions
303(4)
10 Reduced-Complexity Iterative Sphere Detection for Channel-Coded SDMA-OFDM Systems
307(50)
10.1 Introduction
307(4)
10.1.1 Iterative Detection and Decoding Fundamentals
307(3)
10.1.1.1 System Model
307(1)
10.1.1.2 MAP Bit Detection
308(2)
10.1.2
Chapter Contributions and Outline
310(1)
10.2 Channel-Coded Iterative Centre-Shifting SD
311(23)
10.2.1 Generation of the Candidate List
311(5)
10.2.1.1 List Generation and Extrinsic LLR Calculation
311(1)
10.2.1.2 Computational Complexity of LSDs
312(1)
10.2.1.3 Simulation Results and 2D EXIT-Chart Analysis
313(3)
10.2.2 Centre-Shifting Theory for SDs
316(2)
10.2.3 Centre-Shifting K-Best SD-Aided Iterative Receiver Architectures
318(16)
10.2.3.1 Direct Hard-Decision Centre-Update-Based Two-Stage Iterative Architecture
319(1)
10.2.3.1.1 Receiver Architecture and EXIT-Chart-Aided Analysis
319(1)
10.2.3.1.2 Simulation Results
322(2)
10.2.3.2 Two-Stage Iterative Architecture Using a Direct Soft-Decision Centre Update
324(1)
10.2.3.2.1 Soft-Symbol Calculation
325(1)
10.2.3.2.2 Receiver Architecture and EXIT-Chart-Aided Analysis
326(1)
10.2.3.2.3 Simulation Results
328(1)
10.2.3.3 Two-Stage Iterative Architecture Using an Iterative SIC-MMSE-Aided Centre Update
328(1)
10.2.3.3.1 SIC-Aided MMSE Algorithm
329(1)
10.2.3.3.2 Receiver Architecture and EXIT-Chart Analysis
330(1)
10.2.3.3.3 Simulation Results
331(3)
10.3 A Priori LLR-Threshold-Assisted Low-Complexity SD
334(9)
10.3.1 Principle of the ALT-Aided Detector
334(1)
10.3.2 Features of the ALT-Assisted K-Best SD Receiver
335(6)
10.3.2.1 BER Performance Gain
335(1)
10.3.2.2 Computational Complexity
336(2)
10.3.2.3 Choice of LLR Threshold
338(1)
10.3.2.4 Non-Gaussian-Distributed LLRs Caused by the ALT Scheme
339(2)
10.3.3 ALT-Assisted Centre-Shifting Hybrid SD
341(2)
10.3.3.1 Comparison of the Centre-Shifting and the ALT Schemes
341(1)
10.3.3.2 ALT-Assisted Centre-Shifting Hybrid SD
342(1)
10.4 URC-Aided Three-Stage Iterative Receiver Employing SD
343(10)
10.4.1 URC-Aided Three-Stage Iterative Receiver
343(5)
10.4.2 Performance of the Three-Stage Receiver Employing the Centre-Shifting SD
348(1)
10.4.3 Irregular Convolutional Codes for Three-Stage Iterative Receivers
349(4)
10.5
Chapter Conclusions
353(4)
11 Sphere-Packing Modulated STBC-OFDM and its Sphere Detection
357(22)
11.1 Introduction
357(3)
11.1.1 System Model
357(2)
11.1.2
Chapter Contributions and Outline
359(1)
11.2 Orthogonal Transmit Diversity Design with SP Modulation
360(9)
11.2.1 STBCs
360(4)
11.2.1.1 STBC Encoding
360(1)
11.2.1.2 Equivalent STBC Channel Matrix
361(1)
11.2.1.3 STBC Diversity Combining and Maximum Likelihood Detection
362(2)
11.2.1.4 Other STBCs and Orthogonal Designs
364(1)
11.2.2 Orthogonal Design of STBC Using SP Modulation
364(3)
11.2.2.1 Joint Orthogonal Space—Time Signal Design for Two Antennas Using SP
364(3)
11.2.2.2 SP Constellation Construction
367(1)
11.2.3 System Model for STBC-SP-Aided MU-MIMO Systems
367(2)
11.3 Sphere Detection Design for SP Modulation
369(7)
11.3.1 Bit-Based MAP Detection for SP-Modulated MU-MIMO Systems
370(1)
11.3.2 SD Design for SP Modulation
370(4)
11.3.2.1 Transformation of the ML Metric
370(1)
11.3.2.2 Channel Matrix Triangularization
371(1)
11.3.2.3 User-Based Tree Search
371(3)
11.3.3 Simulation Results and Discussion
374(2)
11.4
Chapter Conclusions
376(3)
12 Multiple-Symbol Differential Sphere Detection for Differentially Modulated Cooperative OFDM Systems
379(40)
12.1 Introduction
379(6)
12.1.1 Differential Phase-Shift Keying and Detection
380(3)
12.1.1.1 Conventional Differential Signalling and Detection
380(2)
12.1.1.2 Effects of Time-Selective Channels on Differential Detection
382(1)
12.1.1.3 Effects of Frequency-Selective Channels on Differential Detection
382(1)
12.1.2
Chapter Contributions and Outline
383(2)
12.2 Principle of Single-Path MSDSD
385(5)
12.2.1 ML Metric for MSDD
385(1)
12.2.2 Metric Transformation
386(1)
12.2.3 Complexity Reduction Using SD
387(1)
12.2.4 Simulation Results
387(3)
12.2.4.1 Time-Differential-Encoded OFDM System
387(3)
12.2.4.2 Frequency-Differential-Encoded OFDM System
390(1)
12.3 Multi-path MSDSD Design for Cooperative Communication
390(26)
12.3.1 System Model
390(3)
12.3.2 Differentially Encoded Cooperative Communication Using CDD
393(5)
12.3.2.1 Signal Combining at the Destination for DAF Relaying
393(1)
12.3.2.2 Signal Combining at Destination for DDF Relaying
394(1)
12.3.2.3 Simulation Results
395(3)
12.3.3 Multi-path MSDSD Design for Cooperative Communication
398(11)
12.3.3.1 Derivation of the Metric for Optimum Detection
399(1)
12.3.3.1.1 Equivalent System Model for the DDF-Aided Cooperative Systems
399(1)
12.3.3.1.2 Equivalent System Model for the DAF-Aided Cooperative System
401(1)
12.3.3.1.3 Optimum Detection Metric
402(4)
12.3.3.2 Transformation of the ML Metric
406(2)
12.3.3.3 Channel-Noise Autocorrelation Matrix Triangularization
408(1)
12.3.3.4 Multi-dimensional Tree-Search-Aided MSDSD Algorithm
408(1)
12.3.4 Simulation Results
409(10)
12.3.4.1 Performance of the MSDSD-Aided DAF-User-Cooperation System
409(3)
12.3.4.2 Performance of the MSDSD-Aided DDF User-Cooperation System
412(4)
12.4
Chapter Conclusions
416(3)
13 Resource Allocation for the Differentially Modulated Cooperation-Aided Cellular Uplink in Fast Rayleigh Fading Channels
419(40)
13.1 Introduction
419(2)
13.1.1
Chapter Contributions and Outline
419(1)
13.1.2 System Model
420(1)
13.2 Performance Analysis of the Cooperation-Aided UL
421(11)
13.2.1 Theoretical Analysis of Differential Amplify-and-Forward Systems
421(8)
13.2.1.1 Performance Analysis
421(5)
13.2.1.2 Simulation Results and Discussion
426(3)
13.2.2 Theoretical Analysis of DDF Systems
429(3)
13.2.2.1 Performance Analysis
429(2)
13.2.2.2 Simulation Results and Discussion
431(1)
13.3 CUS for the Uplink
432(17)
13.3.1 CUS for DAF Systems with APC
433(10)
13.3.1.1 APC for DAF-Aided Systems
433(2)
13.3.1.2 CUS Scheme for DAF-Aided Systems
435(2)
13.3.1.3 Simulation Results and Discussion
437(6)
13.3.2 CUS for DDF Systems with APC
443(6)
13.3.2.1 Simulation Results and Discussion
444(5)
13.4 Joint CPS and CUS for the Differential Cooperative Cellular UL Using APC
449(7)
13.4.1 Comparison Between the DAF- and DDF-Aided Cooperative Cellular UL
450(2)
13.4.1.1 Sensitivity to the Source–Relay Link Quality
450(1)
13.4.1.2 Effect of the Packet Length
450(1)
13.4.1.3 Cooperative Resource Allocation
451(1)
13.4.2 Joint CPS and CUS Scheme for the Cellular UL Using APC
452(4)
13.5
Chapter Conclusions
456(3)
14 The Near-Capacity Differentially Modulated Cooperative Cellular Uplink
459(32)
14.1 Introduction
459(4)
14.1.1 System Architecture and Channel Model
460(2)
14.1.1.1 System Model
460(1)
14.1.1.2 Channel Model
461(1)
14.1.2
Chapter Contributions and Outline
462(1)
14.2 Channel Capacity of Non-coherent Detectors
463(2)
14.3 SISO MSDSD
465(7)
14.3.1 Soft-Input Processing
466(3)
14.3.2 Soft-Output Generation
469(1)
14.3.3 Maximum Achievable Rate Versus the Capacity: An EXIT-Chart Perspective
470(2)
14.4 Approaching the Capacity of the Differentially Modulated Cooperative Cellular Uplink
472(15)
14.4.1 Relay-Aided Cooperative Network Capacity
472(5)
14.4.1.1 Perfect-SR-Link DCMC Capacity
472(3)
14.4.1.2 Imperfect-SR-Link DCMC Capacity
475(2)
14.4.2 Ir-DHCD Encoding/Decoding for the Cooperative Cellular Uplink
477(2)
14.4.3 Approaching the Cooperative System's Capacity
479(7)
14.4.3.1 Reduced-Complexity Near-Capacity Design at Relay MS
480(2)
14.4.3.2 Reduced-Complexity Near-Capacity Design at Destination BS
482(4)
14.4.4 Simulation Results and Discussion
486(1)
14.5
Chapter Conclusions
487(4)
Part III Coherent SDM-OFDM Systems 491(106)
List of Symbols in Part III
493(2)
15 Multi-stream Detection for SDM-OFDM Systems
495(20)
15.1 SDM/V-BLAST OFDM Architecture
495(1)
15.2 Linear Detection Methods
496(5)
15.2.1 MMSE Detection
497(4)
15.2.1.1 Generation of Soft-Bit Information for Turbo Decoding
498(1)
15.2.1.2 Performance Analysis of the Linear SDM Detector
499(2)
15.3 Nonlinear SDM Detection Methods
501(8)
15.3.1 ML Detection
501(3)
15.3.1.1 Generation of Soft-Bit Information
503(1)
15.3.1.2 Performance Analysis of the ML SDM Detector
503(1)
15.3.2 SIC Detection
504(3)
15.3.2.1 Performance Analysis of the SIC SDM Detector
506(1)
15.3.3 GA-Aided MMSE Detection
507(2)
15.3.3.1 Performance Analysis of the GA-MMSE SDM Detector
508(1)
15.4 Performance Enhancement Using Space–Frequency Interleaving
509(2)
15.4.1 Space–Frequency-Interleaved OFDM
510(5)
15.4.1.1 Performance Analysis of the SFI-SDM-OFDM
510(1)
15.5 Performance Comparison and Discussion
511(1)
15.6 Conclusions
512(3)
16 Approximate Log-MAP SDM-OFDM Multi-stream Detection
515(34)
16.1 OHRSA-Aided SDM Detection
515(34)
16.1.1 OHRSA-Aided ML SDM Detection
516(8)
16.1.1.1 Search Strategy
518(4)
16.1.1.2 Generalization of the OHRSA-ML SDM Detector
522(2)
16.1.2 Bit-wise OHRSA-ML SDM Detection
524(5)
16.1.2.1 Generalization of the BW-OHRSA-ML SDM Detector
528(1)
16.1.3 OHRSA-Aided Log-MAP SDM Detection
529(8)
16.1.4 Soft-Input, Soft-Output Max-Log-MAP SDM Detection
537(1)
16.1.5 SOPHIE-Aided Approximate Log-MAP SDM Detection
538(16)
16.1.5.1 SOPHIE Algorithm Complexity Analysis
541(2)
16.1.5.2 SOPHIE Algorithm Performance Analysis
543(6)
17 Iterative Channel Estimation and Multi-stream Detection for SDM-OFDM
549(14)
17.1 Iterative Signal Processing
549(1)
17.2 Turbo Forward Error-Correction Coding
550(2)
17.3 Iterative Detection – Decoding
552(2)
17.4 Iterative Channel Estimation – Detection and Decoding
554(6)
17.4.1 Mitigation of Error Propagation
556(1)
17.4.2 MIMO-PASTD-DDCE Aided SDM-OFDM Performance Analysis
557(6)
17.4.2.1 Number of Channel Estimation–Detection Iterations
557(1)
17.4.2.2 Pilot Overhead
557(2)
17.4.2.3 Performance of a Symmetric MIMO System
559(1)
17.4.2.4 Performance of a Rank-Deficient MIMO System
559(1)
17.5
Chapter Summary
560(3)
18 Summary, Conclusions and Future Research
563(34)
18.1 Summary of Results
563(24)
18.1.1 OFDM History, Standards and System Components
563(1)
18.1.2 Channel-Coded STBC-OFDM Systems
563(1)
18.1.3 Coded-Modulation-Assisted Multi-user SDMA-OFDM Using Frequency-Domain Spreading
564(1)
18.1.4 Hybrid Multi-user Detection for SDMA-OFDM Systems
565(2)
18.1.5 DSS and SSCH-Aided Multi-user SDMA-OFDM Systems
567(2)
18.1.6 Channel Estimation for OFDM and MC-CDMA
569(1)
18.1.7 Joint Channel Estimation and MUD for SDMA-OFDM
570(2)
18.1.8 Sphere Detection for Uncoded SDMA-OFDM
572(5)
18.1.8.1 Exploitation of the LLRs Delivered by the Channel Decoder
572(5)
18.1.8.2 EXIT-Chart-Aided Adaptive SD Mechanism
577(1)
18.1.9 Transmit Diversity Schemes Employing SDs
577(2)
18.1.9.1 Generalized Multi-layer Tree Search Mechanism
578(1)
18.1.9.2 Spatial Diversity Schemes Using SDs
578(1)
18.1.10 SD-Aided MIMO System Designs
579(6)
18.1.10.1 Resource-Optimized Hybrid Cooperative System Design
579(2)
18.1.10.2 Near-Capacity Cooperative and Non-cooperative System Designs
581(4)
18.1.11 Multi-stream Detection in SDM-OFDM Systems
585(1)
18.1.12 Iterative Channel Estimation and Multi-stream Detection in SDM-OFDM Systems
585(1)
18.1.13 Approximate Log-MAP SDM-OFDM Multi-stream Detection
586(1)
18.2 Suggestions for Future Research
587(10)
18.2.1 Optimization of the GA MUD Configuration
587(1)
18.2.2 Enhanced FD-CHTF Estimation
588(1)
18.2.3 Radial-Basis-Function-Assisted OFDM
589(1)
18.2.4 Non-coherent Multiple-Symbol Detection in Cooperative OFDM Systems
590(2)
18.2.5 Semi-Analytical Wireless System Model
592(5)
A Appendix to
Chapter 5		 597(6)
A.1 A Brief Introduction to Genetic Algorithms
597(4)
A.2 Normalization of the Mutation-Induced Transition Probability
601(2)
Glossary 603(8)
Bibliography 611(30)
Subject Index 641(6)
Author Index 647
Dr Lajos Hanzo, School of Electronics and Computer Science, University of Southampton Lajos is Professor of Wireless multimedia communications in the School of Electronics and Computer Science. He has over 30 years experience in communications and has held various academic posts in Hungary, Germany and the UK. He has been a member of the academic staff at Southampton University since 1986 where he currently holds the Chair in Telecommunications. Professor Hanzo has published 12 titles with Wiley/IEEE and has published about 700 research papers. Dr Yosef (Jos) Akhtman, School of Electronics and Computer Science, University of Southampton Jos Akhtman is currently working as a Senior Research Assistant in the Communications Group, ECS. His major subject of interest is optimization algorithms for advanced multi-antenna multi-carrier communication systems. Specifically, iterative channel estimation, detection, space-time processing and turbo transceiver architecture. From 2000 to 2002 he was working as a Research Engineer in VYYO Ltd., Jerusalem, Israel and has co-authored numerous journal articles and book chapters.

Dr Ming Jiang Advanced Technology, Standards and Regulation (ATSR) of Samsung Electronics Research Institute (SERI), UK Since April 2006, Dr. Jiang has been with Advanced Technology, Standards and Regulation (ATSR) of Samsung Electronics Research Institute (SERI), working on the European FP6 WINNER project as well as on internal projects related to advanced wireless communication systems. From 2002 to 2005, he was involved in the Core 3 research project of the Mobile Virtual Centre of Excellence (VCE), UK on air-interface algorithms designed for MIMO OFDM systems. His research interests include multi-user detection, channel estimation, space-time processing, heuristic and adaptive optimization, frequency-hopping and MIMO OFDM and OFDMA systems. Dr. Jiang has co-authored one IEEE Press book chapter and several IEE/IEEE journal and conference papers.
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