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E-grāmata: Wireless Communications - Algorithmic Techniques: Algorithmic Techniques [Wiley Online]

(University of Canterbury, Christchurch, New Zealand), (University of Canterbury, Christchurch, New Zealand), (University of Parma, Italy), (University of Modena and Reggio Emilia, Italy), (University of Modena and Reggio Emilia, Italy)
  • Formāts: 744 pages
  • Izdošanas datums: 03-May-2013
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1118576616
  • ISBN-13: 9781118576618
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  • Wiley Online
  • Cena: 115,09 €*
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Wireless Communications - Algorithmic Techniques: Algorithmic TechniquesPalielināt
  • Formāts: 744 pages
  • Izdošanas datums: 03-May-2013
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1118576616
  • ISBN-13: 9781118576618
Citas grāmatas par šo tēmu:
Wiley Online E-booksMore info about Wiley Online
Rokasgrāmatas mājas lapa: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118576618
This book introduces the theoretical elements at the basis of various classes of algorithms commonly employed in the physical layer (and, in part, in MAC layer) of wireless communications systems. It focuses on single user systems, so ignoring multiple access techniques. Moreover, emphasis is put on single-input single-output (SISO) systems, although some relevant topics about multiple-input multiple-output (MIMO) systems are also illustrated.

Comprehensive wireless specific guide to algorithmic techniques Provides a detailed analysis of channel equalization and channel coding for wireless applications Unique conceptual approach focusing in single user systems Covers algebraic decoding, modulation techniques, channel coding and channel equalisation 
Preface xi
List of Acronyms
xiii
1 Introduction
1(10)
1.1 Structure of a Digital Communication System
3(4)
1.2 Plan of the Book
7(1)
1.3 Further Reading
8(3)
Part I MODULATION AND DETECTION
2 Wireless Channels
11(54)
2.1 Introduction
11(5)
2.2 Mathematical Description of SISO Wireless Channels
16(28)
2.2.1 Input-Output Characterization of a SISO Wireless Channel
16(7)
2.2.2 Statistical Characterization of a SISO Wireless Channel
23(13)
2.2.3 Reduced-Complexity Statistical Models for SISO Channels
36(8)
2.3 Mathematical Description and Modeling of MIMO Wireless Channels
44(13)
2.3.1 Input-Output Characterization of a MIMO Wireless Channel
45(5)
2.3.2 Statistical Characterization of a MIMO Wireless Channel
50(7)
2.3.3 Reduced-Complexity Statistical Modeling of MIMO Channels
57(1)
2.4 Historical Notes
57(7)
2.4.1 Large-Scale Fading Models
58(2)
2.4.2 Small-Scale Fading Models
60(4)
2.5 Further Reading
64(1)
3 Digital Modulation Techniques
65(90)
3.1 Introduction
65(1)
3.2 General Structure of a Digital Modulator
65(3)
3.3 Representation of Digital Modulated Waveforms on an Orthonormal Basis
68(2)
3.4 Bandwidth of Digital Modulations
70(4)
3.5 Passband PAM
74(12)
3.5.1 Signal Model
74(2)
3.5.2 Constellation Selection
76(3)
3.5.3 Data Block Transmission with Passband PAM Signals for Frequency-Domain Equalization
79(1)
3.5.4 Power Spectral Density of Linear Modulations
80(6)
3.6 Continuous Phase Modulation
86(30)
3.6.1 Signal Model
86(3)
3.6.2 Full-Response CPM
89(4)
3.6.3 Partial-Response CPM
93(5)
3.6.4 Multi-h CPM
98(2)
3.6.5 Alternative Representations of CPM Signals
100(7)
3.6.6 Data Block Transmission with CPM Signals for Frequency-Domain Equalization
107(3)
3.6.7 Power Spectral Density of Continuous Phase Modulations
110(6)
3.7 OFDM
116(21)
3.7.1 Introduction
116(6)
3.7.2 OFDM Signal Model
122(9)
3.7.3 Power Spectral Density of OFDM
131(4)
3.7.4 The PAPR Problem in OFDM
135(2)
3.8 Lattice-Based Multidimensional Modulations
137(9)
3.8.1 Lattices: Basic Definitions and Properties
137(7)
3.8.2 Elementary Constructions of Lattices
144(2)
3.9 Spectral Properties of a Digital Modulation at the Output of a Wireless Channel
146(3)
3.10 Historical Notes
149(5)
3.10.1 Passband PAM Signaling
149(2)
3.10.2 CPM Signaling
151(1)
3.10.3 MCM Signaling
152(1)
3.10.4 Power Spectral Density of Digital Modulations
153(1)
3.11 Further Reading
154(1)
4 Detection of Digital Signals over Wireless Channels: Decision Rules
155(62)
4.1 Introduction
155(1)
4.2 Wireless Digital Communication Systems: Modeling, Receiver Architecture and Discretization of the Received Signal
156(3)
4.2.1 General Model of a Wireless Communication System
156(1)
4.2.2 Receiver Architectures
157(2)
4.3 Optimum Detection in a Vector Communication System
159(9)
4.3.1 Description of a Vector Communication System
159(1)
4.3.2 Detection Strategies and Error Probabilities
159(3)
4.3.3 MAP and ML Detection Strategies
162(5)
4.3.4 Diversity Reception and Some Useful Theorems about Data Detection
167(1)
4.4 Mathematical Models for the Receiver Vector
168(20)
4.4.1 Extraction of a Set of Sufficient Statistics from the Received Signal
169(8)
4.4.2 Received Vector for PAM Signaling
177(4)
4.4.3 Received Vector for CPM Signaling
181(3)
4.4.4 Received Vector for OFDM Signaling
184(4)
4.5 Decision Strategies in the Presence of Channel Parameters: Optimal Metrics and Performance Bounds
188(19)
4.5.1 Signal Model and Algorithm Classification
188(1)
4.5.2 Detection for Transmission over of a Known Channel
189(9)
4.5.3 Detection in the Presence of a Statistically Known Channel
198(7)
4.5.4 Detection in the Presence of an Unknown Channel
205(2)
4.6 Expectation-Maximization Techniques for Data Detection
207(7)
4.6.1 The EM Algorithm
207(3)
4.6.2 The Bayesian EM Algorithm
210(3)
4.6.3 Initialization and Convergence of EM-Type Algorithms
213(1)
4.6.4 Other EM Techniques
213(1)
4.7 Historical Notes
214(2)
4.8 Further Reading
216(1)
5 Data-Aided Algorithms for Channel Estimation
217(32)
5.1 Channel Estimation Techniques
218(10)
5.1.1 Introduction
218(1)
5.1.2 Feedforward Estimation
219(3)
5.1.3 Recursive Estimation
222(5)
5.1.4 The Principle of Per-Survivor Processing
227(1)
5.2 Cramer-Rao Bounds for Data-Aided Channel Estimation
228(7)
5.3 Data-Aided CIR Estimation Algorithms in PATs
235(9)
5.3.1 PAT Modeling and Optimization
235(3)
5.3.2 A Signal Processing Perspective on PAT Techniques
238(6)
5.4 Extensions to MIMO Channels
244(1)
5.4.1 Channel Estimation in SC MIMO PATs
244(1)
5.4.2 Channel Estimation in MC MIMO PATs
245(1)
5.5 Historical Notes
245(2)
5.6 Further Reading
247(2)
6 Detection of Digital Signals over Wireless Channels: Channel Equalization Algorithms
249(74)
6.1 Introduction
249(1)
6.2 Channel Equalization of Single-Carrier Modulations: Known CIR
250(36)
6.2.1 Channel Equalization in the Time Domain
250(31)
6.2.2 Channel Equalization in the Frequency Domain
281(5)
6.3 Channel Equalization of Multicarrier Modulations: Known CIR
286(6)
6.3.1 Optimal Detection in the Absence of IBI and ICI
287(2)
6.3.2 ICI Cancelation Techniques for Time-Varying Channels
289(3)
6.3.3 Equalization Strategies for IBI Compensation
292(1)
6.4 Channel Equalization of Single Carrier Modulations: Statistically Known CIR
292(9)
6.4.1 MLSD
292(7)
6.4.2 Other Equalization Strategies with Frequency-Flat Fading
299(2)
6.5 Channel Equalization of Multicarrier Modulations: Statistically Known CIR
301(1)
6.6 Joint Channel and Data Estimation: Single-Carrier Modulations
302(5)
6.6.1 Adaptive MLSD
302(1)
6.6.2 PSP MLSD
303(2)
6.6.3 Adaptive MAPBD/MAPSD
305(1)
6.6.4 Equalization Strategies Employing Reference-Based Channel Estimators with Frequency-Flat Fading
306(1)
6.7 Joint Channel and Data Estimation: Multicarrier Modulations
307(4)
6.7.1 Pilot-Based Equalization Techniques
308(2)
6.7.2 Semiblind Equalization Techniques
310(1)
6.8 Extensions to the MIMO Systems
311(4)
6.8.1 Equalization Techniques for Single-Carrier MIMO Communications
311(3)
6.8.2 Equalization Techniques for MIMO-OFDM Communications
314(1)
6.9 Historical Notes
315(4)
6.10 Further Reading
319(4)
Part II INFORMATION THEORY AND CODING SCHEMES
7 Elements of Information Theory
323(16)
7.1 Introduction
323(1)
7.2 Capacity for Discrete Sources and Channels
323(7)
7.2.1 The Discrete Memoryless Channel
324(1)
7.2.2 The Continuous-Output Channel
325(1)
7.2.3 Channel Capacity
326(4)
7.3 Capacity of MIMO Fading Channels
330(7)
7.3.1 Frequency-Flat Fading Channel
330(2)
7.3.2 MIMO Channel Capacity
332(3)
7.3.3 Random Channel
335(2)
7.4 Historical Notes
337(1)
7.5 Further Reading
338(1)
8 An Introduction to Channel Coding Techniques
339(10)
8.1 Basic Principles
339(2)
8.2 Interleaving
341(2)
8.3 Taxonomy of Channel Codes
343(1)
8.4 Taxonomy of Coded Modulations
344(2)
8.5 Organization of the Following
Chapters
346(1)
8.6 Historical Notes
346(1)
8.7 Further Reading
347(2)
9 Classical Coding Schemes
349(92)
9.1 Block Codes
349(41)
9.1.1 Introduction
349(1)
9.1.2 Structure of Linear Codes over GF(q)
350(2)
9.1.3 Properties of Linear Block Codes
352(5)
9.1.4 Cyclic Codes
357(12)
9.1.5 Other Relevant Linear Block Codes
369(2)
9.1.6 Decoding Techniques for Block Codes
371(17)
9.1.7 Error Performance
388(2)
9.2 Convolutional Codes
390(42)
9.2.1 Introduction
390(4)
9.2.2 Properties of Convolutional Codes
394(14)
9.2.3 Maximum Likelihood Decoding of Convolutional Codes
408(5)
9.2.4 MAP Decoding of Convolutional Codes
413(6)
9.2.5 Sequential Decoding of Convolutional Codes
419(3)
9.2.6 Error Performance of ML Decoding of Convolutional Codes
422(10)
9.3 Classical Concatenated Coding
432(3)
9.3.1 Parallel Concatenation: Product Codes
432(2)
9.3.2 Serial Concatenation: Outer RS Code
434(1)
9.4 Historical Notes
435(4)
9.4.1 Algebraic Coding
435(3)
9.4.2 Probabilistic Coding
438(1)
9.5 Further Reading
439(2)
10 Modern Coding Schemes
441(64)
10.1 Introduction
441(1)
10.2 Concatenated Convolutional Codes
442(3)
10.2.1 Parallel Concatenated Coding Schemes
442(2)
10.2.2 Serially Concatenated Coding Schemes
444(1)
10.2.3 Hybrid Concatenated Coding Schemes
445(1)
10.3 Concatenated Block Codes
445(1)
10.4 Other Modern Concatenated Coding Schemes
446(2)
10.4.1 Repeat and Accumulate Codes
446(1)
10.4.2 Serial Concatenation of Coding Schemes and Differential Modulations
447(1)
10.5 Iterative Decoding Techniques for Concatenated Codes
448(20)
10.5.1 The Turbo Principle
448(7)
10.5.2 SiSo Decoding Algorithms
455(4)
10.5.3 Applications
459(6)
10.5.4 Performance Bounds
465(3)
10.6 Low-Density Parity Check Codes
468(10)
10.6.1 Definition and Classification
468(1)
10.6.2 Graphic Representation of LDPC Codes via Tanner Graphs
468(3)
10.6.3 Minimum Distance and Weight Spectrum
471(1)
10.6.4 LDPC Code Design Approaches
472(5)
10.6.5 Efficient Algorithms for LDPC Encoding
477(1)
10.7 Decoding Techniques for LDPC Codes
478(16)
10.7.1 Introduction to Decoding via Message Passing Algorithms
478(3)
10.7.2 SPA and MSA
481(8)
10.7.3 Technical Issues on LDPC Decoding via MP
489(5)
10.8 Codes on Graphs
494(7)
10.9 Historical Notes
501(2)
10.10 Further Reading
503(2)
11 Signal Space Codes
505(62)
11.1 Introduction
505(1)
11.2 Trellis Coding with Expanded Signal Sets
505(15)
11.2.1 Code Construction
506(11)
11.2.2 Decoding Algorithms
517(1)
11.2.3 Error Performance
518(2)
11.3 Bit-Interleaved Coded Modulation
520(4)
11.3.1 Code Construction
520(1)
11.3.2 Decoding Algorithms
521(1)
11.3.3 Error Performance
522(2)
11.4 Modulation Codes Based on Multilevel Coding
524(7)
11.4.1 Code Construction for AWGN Channels
524(4)
11.4.2 Multistage Decoder
528(1)
11.4.3 Error Performance
529(1)
11.4.4 Multilevel Codes for Rayleigh Flat Fading Channels
530(1)
11.5 Space-Time Coding
531(34)
11.5.1 ST Coding for Frequency-Flat Fading Channels
531(30)
11.5.2 ST Coding for Frequency-Selective Fading Channels
561(4)
11.6 Historical Notes
565(1)
11.7 Further Reading
566(1)
12 Combined Equalization and Decoding
567(24)
12.1 Introduction
567(1)
12.2 Noniterative Techniques
568(3)
12.3 Algorithms for Combined Equalization and Decoding
571(15)
12.3.1 Introduction
571(4)
12.3.2 Turbo Equalization from a FG Perspective
575(5)
12.3.3 Reduced-Complexity Techniques for SiSo Equalization
580(3)
12.3.4 Turbo Equalization in the FD
583(2)
12.3.5 Turbo Equalization in the Presence of an Unknown Channel
585(1)
12.4 Extension to MIMO
586(2)
12.5 Historical Notes
588(2)
12.5.1 Reduced-Complexity SiSo Equalization
588(1)
12.5.2 Error Performance and Convergence Speed in Turbo Equalization
588(1)
12.5.3 SiSo Equalization Algorithms in the Frequency Domain
589(1)
12.5.4 Use of Precoding
589(1)
12.5.5 Turbo Equalization and Factor Graphs
589(1)
12.5.6 Turbo Equalization for MIMO Systems
589(1)
12.5.7 Related Techniques
590(1)
12.6 Further Reading
590(1)
Appendix A Fourier Transforms
591(2)
Appendix B Power Spectral Density of Random Processes
593(4)
B.1 Power Spectral Density of a Wide-Sense Stationary Random Process
593(1)
B.2 Power Spectral Density of a Wide-Sense Cyclostationary Random Process
594(1)
B.3 Power Spectral Density of a Bandpass Random Process
595(2)
Appendix C Matrix Theory
597(4)
Appendix D Signal Spaces
601(8)
D.1 Representation of Deterministic Signals
601(5)
D.1.1 Basic Definitions
601(1)
D.1.2 Representation of Deterministic Signals via Orthonormal Bases
602(4)
D.2 Representation of Random Signals via Orthonormal Bases
606(3)
Appendix E Groups, Finite Fields and Vector Spaces
609(16)
E.1 Groups
609(2)
E.2 Fields
611(11)
E.2.1 Axiomatic Definition of a Field and Finite Fields
611(1)
E.2.2 Polynomials and Extension Fields
612(4)
E.2.3 Other Definitions and Properties
616(4)
E.2.4 Computation Techniques for Finite Fields
620(2)
E.3 Vector Spaces
622(3)
Appendix F Error Function and Related Functions
625(4)
References 629(84)
Index 713
Giorgio M. Vitetta is a Full Professor of Telecommunications at the Department of Information Engineering of the University of Modena and Reggio Emilia. He received the Dr. Ing. Degree in Electronic Engineering (cum Laude) in 1990 and the Ph. D. degree in 1994, both from the University of Pisa, Italy.

Desmond Taylor is the Tait Professor of Communications at the University of Canterbury. He gained his PhD in Electrical Engineering from McMaster University in Canada. He specializes in Digital Communication Systems. He is the director of journals for the IEEE Communications Society.

Philippa Martin is a lecturer in Electrical Engineering at the University of Canterbury. Her research interests include coded modulation, error correction coding and decoding, reduced complexity decoding algorithms, iterative processing, space-time coding, detection and decoding, and combined equalization and decoding.

Fabrizio Pancaldi received his Dr. Ing. Degree in Electronic Engineering (cum laude) and a Ph. D. degree in 2006, both from the University of Modena and Reggio Emilia, Italy. He is currently a Research Fellow and lectures in Telecommunication Networks.
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