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Informationen zum Autor 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. Klappentext Offers the most complete, up-to-date coverage available on the principles of digital communications. Focuses on basic issues, relating theory to practice wherever possible. Numerous examples, worked out in detail, have been included to help the reader develop an intuitive grasp of the theory. Topics covered include the sampling process, digital modulation techniques, error-control coding, robust quantization for pulse-code modulation, coding speech at low bit radio, information theoretic concepts, coding and computer communication. Because the book covers a broad range of topics in digital communications, it should satisfy a variety of backgrounds and interests. Zusammenfassung Offers the most complete, up-to-date coverage available on the principles of digital communications. Focuses on basic issues, relating theory to practice wherever possible. Numerous examples, worked out in detail, have been included to help the reader develop an intuitive grasp of the theory. Inhaltsverzeichnis 1 Introduction 1 1.1 Historical Background 1 1.2 The Communication Process 2 1.3 Multiple-Access Techniques 4 1.4 Networks 6 1.5 Digital Communications 9 1.6 Organization of the Book 11 2 Fourier Analysis of Signals and Systems 13 2.1 Introduction 13 2.2 The Fourier Series 13 2.3 The Fourier Transform 16 2.4 The Inverse Relationship between Time-Domain and Frequency-Domain Representations 25 2.5 The Dirac Delta Function 28 2.6 Fourier Transforms of Periodic Signals 34 2.7 Transmission of Signals through Linear Time-Invariant Systems 37 2.8 Hilbert Transform 42 2.9 Pre-envelopes 45 2.10 Complex Envelopes of Band-Pass Signals 47 2.11 Canonical Representation of Band-Pass Signals 49 2.12 Complex Low-Pass Representations of Band-Pass Systems 52 2.13 Putting the Complex Representations of Band-Pass Signals and Systems All Together 54 2.14 Linear Modulation Theory 58 2.15 Phase and Group Delays 66 2.16 Numerical Computation of the Fourier Transform 69 2.17 Summary and Discussion 78 3 Probability Theory and Bayesian Inference 87 3.1 Introduction 87 3.2 Set Theory 88 3.3 Probability Theory 90 3.4 Random Variables 97 3.5 Distribution Functions 98 3.6 The Concept of Expectation 105 3.7 Second-Order Statistical Averages 108 3.8 Characteristic Function 111 3.9 The Gaussian Distribution 113 3.10 The Central Limit Theorem 118 3.11 Bayesian Inference 119 3.12 Parameter Estimation 122 3.13 Hypothesis Testing 126 3.14 Composite Hypothesis Testing 132 3.15 Summary and Discussion 133 4 Stochastic Processes 145 4.1 Introduction 145 4.2 Mathematical Definition of a Stochastic Process 145 4.3 Two Classes of Stochastic Processes: Strictly Stationary and Weakly Stationary 147 4.4 Mean, Correlation, and Covariance Functions of Weakly Stationary Processes 149 4.5 Ergodic Processes 1...
Sommario
Chapters
* Introduction
* Fourier Analysis of Signals and Systems
* Probability Theory and Bayesian Inference
* Stochastic Processes
* Information Theory
* Conversion of Analog Waveforms into Coded Pulses
* Signaling over AWGN Channels
* Signaling over Band-Limited Channels
* Signaling over Fading Channels
* Error-control Coding
Appendices
* Advanced Probabilistic Models
* Bounds on the Q-Function
* Bessel Functions
* Method of Lagrange Multipliers
* Information Capacity of MIMO Channels
* Interleaving
* The Peak-Power Reduction Problem in OFDM
* Nonlinear Solid-State Power Amplifiers
* Monte Carlo Integration
* Maximal-Length Sequences
Mathematical Tables
Glossary
Bibliography
Index
