Digital Communication Systems

Simon Haykin is a University Professor at McMaster University, Hamilton, Ontario, Canada. His research interests include nonlinear dynamics, neural networks and adaptive filters and their applications in radar and communications systems. Dr. Haykin is the editor for a series of books on "Adaptive and Learning Systems for Signal Processing, Communications and Control" published by John Wiley & Sons, Inc. He is both an IEEE Fellow and Fellow of the Royal Society of Canada.

• 1 Introduction 11.1 Historical Background 11.2 The Communication Process 21.3 Multiple-Access Techniques 41.4 Networks 61.5 Digital Communications 91.6 Organization of the Book 112 Fourier Analysis of Signals and Systems 132.1 Introduction 132.2 The Fourier Series 132.3 The Fourier Transform 162.4 The Inverse Relationship between Time-Domain and Frequency-Domain Representations 252.5 The Dirac Delta Function 282.6 Fourier Transforms of Periodic Signals 342.7 Transmission of Signals through Linear Time-Invariant Systems 372.8 Hilbert Transform 422.9 Pre-envelopes 452.10 Complex Envelopes of Band-Pass Signals 472.11 Canonical Representation of Band-Pass Signals 492.12 Complex Low-Pass Representations of Band-Pass Systems 522.13 Putting the Complex Representations of Band-Pass Signals and Systems All Together 542.14 Linear Modulation Theory 582.15 Phase and Group Delays 662.16 Numerical Computation of the Fourier Transform 692.17 Summary and Discussion 783 Probability Theory and Bayesian Inference 873.1 Introduction 873.2 Set Theory 883.3 Probability Theory 903.4 Random Variables 973.5 Distribution Functions 983.6 The Concept of Expectation 1053.7 Second-Order Statistical Averages 1083.8 Characteristic Function 1113.9 The Gaussian Distribution 1133.10 The Central Limit Theorem 1183.11 Bayesian Inference 1193.12 Parameter Estimation 1223.13 Hypothesis Testing 1263.14 Composite Hypothesis Testing 1323.15 Summary and Discussion 1334 Stochastic Processes 1454.1 Introduction 1454.2 Mathematical Definition of a Stochastic Process 1454.3 Two Classes of Stochastic Processes: Strictly Stationary and Weakly Stationary 1474.4 Mean, Correlation, and Covariance Functions of Weakly Stationary Processes 1494.5 Ergodic Processes 1574.6 Transmission of a Weakly Stationary Process through a Linear Time-invariant Filter 1584.7 Power Spectral Density of a Weakly Stationary Process 1604.8 Another Definition of the Power Spectral Density 1704.9 Cross-spectral Densities 1724.10 The Poisson Process 1744.11 The Gaussian Process 1764.12 Noise 1794.13 Narrowband Noise 1834.14 Sine Wave Plus Narrowband Noise 1934.15 Summary and Discussion 1955 Information Theory 2075.1 Introduction 2075.2 Entropy 2075.3 Source-coding Theorem 2145.4 Lossless Data Compression Algorithms 2155.5 Discrete Memoryless Channels 2235.6 Mutual Information 2265.7 Channel Capacity 2305.8 Channel-coding Theorem 2325.9 Differential Entropy and Mutual Information for Continuous Random Ensembles 2375.10 Information Capacity Law 2405.11 Implications of the Information Capacity Law 2445.12 Information Capacity of Colored Noisy Channel 2485.13 Rate Distortion Theory 2535.14 Summary and Discussion 2566 Conversion of Analog Waveforms into Coded Pulses 2676.1 Introduction 2676.2 Sampling Theory 2686.3 Pulse-Amplitude Modulation 2746.4 Quantization and its Statistical Characterization 2786.5 Pulse-Code Modulation 2856.6 Noise Considerations in PCM Systems 2906.7 Prediction-Error Filtering for Redundancy Reduction 2946.8 Differential Pulse-Code Modulation 3016.9 Delta Modulation 3056.10 Line Codes 3096.11 Summary and Discussion 3127 Signaling over AWGN Channels 3237.1 Introduction 3237.2 Geometric Representation of Signals 3247.3 Conversion of the Continuous AWGN Channel into a Vector Channel 3327.4 Optimum Receivers Using Coherent Detection 3377.5 Probability of Error 3447.6 Phase-Shift Keying Techniques Using Coherent Detection 3527.7 M-ary Quadrature Amplitude Modulation 3707.8 Frequency-Shift Keying Techniques Using Coherent Detection 3757.9 Comparison of M-ary PSK and M-ary FSK from anInformation-Theoretic Viewpoint 3987.10 Detection of Signals with Unknown Phase 4007.11 Noncoherent Orthogonal Modulation Techniques 4047.12 Binary Frequency-Shift Keying Using Noncoherent Detection 4107.13 Differential Phase-Shift Keying 4117.14 BER Comparison of Signaling Schemes over AWGN Channels 4157.15 Synchronization 4187.16 Recursive Maximum Likelihood Estimation for Synchronization 4197.17 Summary and Discussion 4318 Signaling over Band-Limited Channels 4458.1 Introduction 4458.2 Error Rate Due to Channel Noise in a Matched-Filter Receiver 4468.3 Intersymbol Interference 4478.4 Signal Design for Zero ISI 4508.5 Ideal Nyquist Pulse for Distortionless Baseband Data Transmission 4508.6 Raised-Cosine Spectrum 4548.7 Square-Root Raised-Cosine Spectrum 4588.8 Post-Processing Techniques: The Eye Pattern 4638.9 Adaptive Equalization 4698.10 Broadband Backbone Data Network: Signaling over Multiple Baseband Channels 4748.11 Digital Subscriber Lines 4758.12 Capacity of AWGN Channel Revisited 4778.13 Partitioning Continuous-Time Channel into a Set of Subchannels 4788.14 Water-Filling Interpretation of the Constrained Optimization Problem 4848.15 DMT System Using Discrete Fourier Transform 4878.16 Summary and Discussion 4949 Signaling over Fading Channels 5019.1 Introduction 5019.2 Propagation Effects 5029.3 Jakes Model 5069.4 Statistical Characterization of Wideband Wireless Channels 5119.5 FIR Modeling of Doubly Spread Channels 5209.6 Comparison of Modulation Schemes: Effects of Flat Fading 5259.7 Diversity Techniques 5279.8 “Space Diversity-on-Receive” Systems 5289.9 “Space Diversity-on-Transmit” Systems 5389.10 “Multiple-Input, Multiple-Output” Systems: Basic Considerations 5469.11 MIMO Capacity for Channel Known at the Receiver 5519.12 Orthogonal Frequency Division Multiplexing 5569.13 Spread Spectrum Signals 5579.14 Code-Division Multiple Access 5609.15 The RAKE Receiver and Multipath Diversity 5649.16 Summary and Discussion 56610 Error-Control Coding 57710.1 Introduction 57710.2 Error Control Using Forward Error Correction 57810.3 Discrete Memoryless Channels 57910.4 Linear Block Codes 58210.5 Cyclic Codes 59310.6 Convolutional Codes 60510.7 Optimum Decoding of Convolutional Codes 61310.8 Maximum Likelihood Decoding of Convolutional Codes 61410.9 Maximum a Posteriori Probability Decoding of Convolutional Codes 62310.10 Illustrative Procedure for Map Decoding in the Log-Domain 63810.11 New Generation of Probabilistic Compound Codes 64410.12 Turbo Codes 64510.13 EXIT Charts 65710.14 Low-Density Parity-Check Codes 66610.15 Trellis-Coded Modulation 67510.16 Turbo Decoding of Serial Concatenated Codes 68110.17 Summary and Discussion 688AppendicesA Advanced Probabilistic Models A1A.1 The Chi-Square Distribution A1A.2 The Log-Normal Distribution A3A.3 The Nakagami Distribution A6B Bounds on the Q-Function A11C Bessel Functions A13C.1 Series Solution of Bessel’s Equation A13C.2 Properties of the Bessel Function A14C.3 Modified Bessel Function A16D Method of Lagrange Multipliers A19D.1 Optimization Involving a Single Equality Constraint A19E Information Capacity of MIMO Channels A21E.1 Log-Det Capacity Formula of MIMO Channels A21E.2 MIMO Capacity for Channel Known at the Transmitter A24F Interleaving A29F.1 Block Interleaving A30F.2 Convolutional Interleaving A32F.3 Random Interleaving A33G The Peak-Power Reduction Problem in OFDM A35G.1 PAPR Properties of OFDM Signals A35G.2 Maximum PAPR in OFDM Using M-ary PSK A36G.3 Clipping-Filtering: A Technique for PAPR Reduction A37H Nonlinear Solid-State Power Amplifiers A39H.1 Power Amplifier Nonlinearities A39H.2 Nonlinear Modeling of Band-Pass Power Amplifiers A42I Monte Carlo Integration A45J Maximal-Length Sequences A47J.1 Properties of Maximal-Length Sequences A47J.2 Choosing a Maximal-Length Sequence A50K Mathematical Tables A55Glossary G1Bibliography B1Index I1Credits C1