Wireless Communications Algorithmic Techniques

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  • Edition: 1st
  • Format: eBook
  • Copyright: 2013-03-29
  • Publisher: Wiley

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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 

Table of Contents


List of Acronyms

1 Introduction

1.1 Structure of a Digital Communication System

1.2 Plan of the Book

1.3 Further Reading


2 Wireless Channels

2.1 Introduction

2.2 Mathematical Description of SISO Wireless Channels

2.2.1 Input–Output Characterization of a SISO Wireless Channel

2.2.2 Statistical Characterization of a SISO Wireless Channel

2.2.3 Reduced-Complexity Statistical Models for SISO Channels

2.3 Mathematical Description and Modeling of MIMO Wireless Channels

2.3.1 Input–Output Characterization of a MIMO Wireless Channel

2.3.2 Statistical Characterization of a MIMO Wireless Channel

2.3.3 Reduced-Complexity Statistical Modeling of MIMO Channels

2.4 Historical Notes

2.4.1 Large-Scale Fading Models

2.4.2 Small-Scale Fading Models

2.5 Further Reading

3 Digital Modulation Techniques

3.1 Introduction

3.2 General Structure of a Digital Modulator

3.3 Representation of Digital Modulated Waveforms on an Orthonormal Basis

3.4 Bandwidth of Digital Modulations

3.5 Passband PAM

3.5.1 Signal Model

3.5.2 Constellation Selection

3.5.3 Data Block Transmission with Passband PAM Signals for Frequency-Domain Equalization

3.5.4 Power Spectral Density of Linear Modulations

3.6 Continuous Phase Modulation

3.6.1 Signal Model

3.6.2 Full-Response CPM

3.6.3 Partial-Response CPM

3.6.4 Multi-h CPM

3.6.5 Alternative Representations of CPM Signals

3.6.6 Data Block Transmission with CPM Signals for Frequency-Domain Equalization

3.6.7 Power Spectral Density of Continuous Phase Modulations

3.7 OFDM

3.7.1 Introduction

3.7.2 OFDM Signal Model

3.7.3 Power Spectral Density of OFDM

3.7.4 The PAPR Problem in OFDM

3.8 Lattice-Based Multidimensional Modulations

3.8.1 Lattices: Basic Definitions and Properties

3.8.2 Elementary Constructions of Lattices

3.9 Spectral Properties of a Digital Modulation at the Output of a Wireless Channel

3.10 Historical Notes

3.10.1 Passband PAM Signaling

3.10.2 CPM Signaling

3.10.3 MCM Signaling

3.10.4 Power Spectral Density of Digital Modulations

3.11 Further Reading

4 Detection of Digital Signals over Wireless Channels: Decision Rules

4.1 Introduction

4.2 Wireless Digital Communication Systems: Modeling, Receiver Architecture and Discretization of the Received Signal

4.2.1 General Model of a Wireless Communication System

4.2.2 Receiver Architectures

4.3 Optimum Detection in a Vector Communication System

4.3.1 Description of a Vector Communication System

4.3.2 Detection Strategies and Error Probabilities

4.3.3 MAP and ML Detection Strategies

4.3.4 Diversity Reception and Some Useful Theorems about Data Detection

4.4 Mathematical Models for the Receiver Vector

4.4.1 Extraction of a Set of Sufficient Statistics from the Received Signal

4.4.2 Received Vector for PAM Signaling

4.4.3 Received Vector for CPM Signaling

4.4.4 Received Vector for OFDM Signaling

4.5 Decision Strategies in the Presence of Channel Parameters: Optimal Metrics and Performance Bounds

4.5.1 Signal Model and Algorithm Classification

4.5.2 Detection for Transmission over of a Known Channel

4.5.3 Detection in the Presence of a Statistically Known Channel

4.5.4 Detection in the Presence of an Unknown Channel

4.6 Expectation–Maximization Techniques for Data Detection

4.6.1 The EM Algorithm

4.6.2 The Bayesian EM Algorithm

4.6.3 Initialization and Convergence of EM-Type Algorithms

4.6.4 Other EM Techniques

4.7 Historical Notes

4.8 Further Reading

5 Data-Aided Algorithms for Channel Estimation

5.1 Channel Estimation Techniques

5.1.1 Introduction

5.1.2 Feedforward Estimation

5.1.3 Recursive Estimation

5.1.4 The Principle of Per-Survivor Processing

5.2 Cram´er–Rao Bounds for Data-Aided Channel Estimation

5.3 Data-Aided CIR Estimation Algorithms in PATs

5.3.1 PAT Modeling and Optimization

5.3.2 A Signal Processing Perspective on PAT Techniques

5.4 Extensions to MIMO Channels

5.4.1 Channel Estimation in SC MIMO PATs

5.4.2 Channel Estimation in MC MIMO PATs

5.5 Historical Notes

5.6 Further Reading

6 Detection of Digital Signals over Wireless Channels: Channel Equalization Algorithms

6.1 Introduction

6.2 Channel Equalization of Single-Carrier Modulations: Known CIR

6.2.1 Channel Equalization in the Time Domain

6.2.2 Channel Equalization in the Frequency Domain

6.3 Channel Equalization of Multicarrier Modulations: Known CIR

6.3.1 Optimal Detection in the Absence of IBI and ICI

6.3.2 ICI Cancelation Techniques for Time-Varying Channels

6.3.3 Equalization Strategies for IBI Compensation

6.4 Channel Equalization of Single Carrier Modulations: Statistically Known CIR

6.4.1 MLSD

6.4.2 Other Equalization Strategies with Frequency-Flat Fading

6.5 Channel Equalization of Multicarrier Modulations: Statistically Known CIR

6.6 Joint Channel and Data Estimation: Single-Carrier Modulations

6.6.1 Adaptive MLSD

6.6.2 PSP MLSD

6.6.3 Adaptive MAPBD/MAPSD

6.6.4 Equalization Strategies Employing Reference-Based Channel Estimators with Frequency-Flat Fading

6.7 Joint Channel and Data Estimation: Multicarrier Modulations

6.7.1 Pilot-Based Equalization Techniques

6.7.2 Semiblind Equalization Techniques

6.8 Extensions to the MIMO Systems

6.8.1 Equalization Techniques for Single-Carrier MIMO Communications

6.8.2 Equalization Techniques for MIMO-OFDM Communications

6.9 Historical Notes

6.10 Further Reading


7 Elements of Information Theory

7.1 Introduction

7.2 Capacity for Discrete Sources and Channels

7.2.1 The Discrete Memoryless Channel

7.2.2 The Continuous-Output Channel

7.2.3 Channel Capacity

7.3 Capacity of MIMO Fading Channels

7.3.1 Frequency-Flat Fading Channel

7.3.2 MIMO Channel Capacity

7.3.3 Random Channel

7.4 Historical Notes

7.5 Further Reading

8 An Introduction to Channel Coding Techniques

8.1 Basic Principles

8.2 Interleaving

8.3 Taxonomy of Channel Codes

8.4 Taxonomy of Coded Modulations

8.5 Organization of the Following Chapters

8.6 Historical Notes

8.7 Further Reading

9 Classical Coding Schemes

9.1 Block Codes

9.1.1 Introduction

9.1.2 Structure of Linear Codes over GF(q)

9.1.3 Properties of Linear Block Codes

9.1.4 Cyclic Codes

9.1.5 Other Relevant Linear Block Codes

9.1.6 Decoding Techniques for Block Codes

9.1.7 Error Performance

9.2 Convolutional Codes

9.2.1 Introduction

9.2.2 Properties of Convolutional Codes

9.2.3 Maximum Likelihood Decoding of Convolutional Codes

9.2.4 MAP Decoding of Convolutional Codes

9.2.5 Sequential Decoding of Convolutional Codes

9.2.6 Error Performance of ML Decoding of Convolutional Codes

9.3 Classical Concatenated Coding

9.3.1 Parallel Concatenation: Product Codes

9.3.2 Serial Concatenation: Outer RS Code

9.4 Historical Notes

9.4.1 Algebraic Coding

9.4.2 Probabilistic Coding

9.5 Further Reading

10 Modern Coding Schemes

10.1 Introduction

10.2 Concatenated Convolutional Codes

10.2.1 Parallel Concatenated Coding Schemes

10.2.2 Serially Concatenated Coding Schemes

10.2.3 Hybrid Concatenated Coding Schemes

10.3 Concatenated Block Codes

10.4 Other Modern Concatenated Coding Schemes

10.4.1 Repeat and Accumulate Codes

10.4.2 Serial Concatenation of Coding Schemes and Differential Modulations

10.5 Iterative Decoding Techniques for Concatenated Codes

10.5.1 The Turbo Principle

10.5.2 SiSo Decoding Algorithms

10.5.3 Applications

10.5.4 Performance Bounds

10.6 Low-Density Parity Check Codes

10.6.1 Definition and Classification

10.6.2 Graphic Representation of LDPC Codes via Tanner Graphs

10.6.3 Minimum Distance and Weight Spectrum

10.6.4 LDPC Code Design Approaches

10.6.5 Efficient Algorithms for LDPC Encoding

10.7 Decoding Techniques for LDPC Codes

10.7.1 Introduction to Decoding via Message Passing Algorithms

10.7.2 SPA and MSA

10.7.3 Technical Issues on LDPC Decoding via MP

10.8 Codes on Graphs

10.9 Historical Notes

10.10 Further Reading

11 Signal Space Codes

11.1 Introduction

11.2 Trellis Coding with Expanded Signal Sets

11.2.1 Code Construction

11.2.2 Decoding Algorithms

11.2.3 Error Performance

11.3 Bit-Interleaved Coded Modulation

11.3.1 Code Construction

11.3.2 Decoding Algorithms

11.3.3 Error Performance

11.4 Modulation Codes Based on Multilevel Coding

11.4.1 Code Construction for AWGN Channels

11.4.2 Multistage Decoder

11.4.3 Error Performance

11.4.4 Multilevel Codes for Rayleigh Flat Fading Channels

11.5 Space-Time Coding

11.5.1 ST Coding for Frequency-Flat Fading Channels

11.5.2 ST Coding for Frequency-Selective Fading Channels

11.6 Historical Notes

11.7 Further Reading

12 Combined Equalization and Decoding

12.1 Introduction

12.2 Noniterative Techniques

12.3 Algorithms for Combined Equalization and Decoding

12.3.1 Introduction

12.3.2 Turbo Equalization from a FG Perspective

12.3.3 Reduced-Complexity Techniques for SiSo Equalization

12.3.4 Turbo Equalization in the FD

12.3.5 Turbo Equalization in the Presence of an Unknown Channel

12.4 Extension to MIMO

12.5 Historical Notes

12.5.1 Reduced-Complexity SiSo Equalization

12.5.2 Error Performance and Convergence Speed in Turbo Equalization

12.5.3 SiSo Equalization Algorithms in the Frequency Domain

12.5.4 Use of Precoding

12.5.5 Turbo Equalization and Factor Graphs

12.5.6 Turbo Equalization for MIMO Systems

12.5.7 Related Techniques

12.6 Further Reading

Appendix A Fourier Transforms

Appendix B Power Spectral Density of Random Processes

B.1 Power Spectral Density of a Wide-Sense Stationary Random Process

B.2 Power Spectral Density of a Wide-Sense Cyclostationary Random Process

B.3 Power Spectral Density of a Bandpass Random Process

Appendix C Matrix Theory

Appendix D Signal Spaces

D.1 Representation of Deterministic Signals

D.1.1 Basic Definitions

D.1.2 Representation of Deterministic Signals via Orthonormal Bases

D.2 Representation of Random Signals via Orthonormal Bases

Appendix E Groups, Finite Fields and Vector Spaces

E.1 Groups

E.2 Fields

E.2.1 Axiomatic Definition of a Field and Finite Fields

E.2.2 Polynomials and Extension Fields

E.2.3 Other Definitions and Properties

E.2.4 Computation Techniques for Finite Fields

E.3 Vector Spaces

Appendix F Error Function and Related Functions



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