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Digital Communication Systems

Book Description

This new text offers up-to-date coverage on the principles of digital communications, focusing on core principles and relating theory to practice.

Numerous examples, worked out in detail, have been included to help the student develop an intuitive grasp of the theory. The text also incorporates MATLAB-based computer experiments throughout, as well as themed examples and an abundance of homework problems.

Table of Contents

  1. Coverpage
  2. Titlepage
  3. Copyright
  4. Dedication
  5. Preface
  6. Contents
  7. 1 Introduction
    1. 1.1 Historical Background
    2. 1.2 The Communication Process
    3. 1.3 Multiple-Access Techniques
    4. 1.4 Networks
    5. 1.5 Digital Communications
    6. 1.6 Organization of the Book
  8. 2 Fourier Analysis of Signals and Systems
    1. 2.1 Introduction
    2. 2.2 The Fourier Series
    3. 2.3 The Fourier Transform
    4. 2.4 The Inverse Relationship between Time-Domain and Frequency-Domain Representations
    5. 2.5 The Dirac Delta Function
    6. 2.6 Fourier Transforms of Periodic Signals
    7. 2.7 Transmission of Signals through Linear Time-Invariant Systems
    8. 2.8 Hilbert Transform
    9. 2.9 Pre-envelope
    10. 2.10 Complex Envelopes of Band-Pass Signals
    11. 2.11 Canonical Representation of Band-Pass Signals
    12. 2.12 Complex Low-Pass Representations of Band-Pass Systems
    13. 2.13 Putting the Complex Representations of Band-Pass Signals and Systems All Together
    14. 2.14 Linear Modulation Theory
    15. 2.15 Phase and Group Delays
    16. 2.16 Numerical Computation of the Fourier Transform
    17. 2.17 Summary and Discussion
  9. 3 Probability Theory and Bayesian Inference
    1. 3.1 Introduction
    2. 3.2 Set Theory
    3. 3.3 Probability Theory
    4. 3.4 Random Variables
    5. 3.5 Distribution Functions
    6. 3.6 The Concept of Expectation
    7. 3.7 Second-Order Statistical Averages
    8. 3.8 Characteristic Function
    9. 3.9 The Gaussian Distribution
    10. 3.10 The Central Limit Theorem
    11. 3.11 Bayesian Inference
    12. 3.12 Parameter Estimation
    13. 3.13 Hypothesis Testing
    14. 3.14 Composite Hypothesis Testing
    15. 3.15 Summary and Discussion
  10. 4 Stochastic Processes
    1. 4.1 Introduction
    2. 4.2 Mathematical Definition of a Stochastic Process
    3. 4.3 Two Classes of Stochastic Processes: Strictly Stationary and Weakly Stationary
    4. 4.4 Mean, Correlation, and Covariance Functions of Weakly Stationary Processes
    5. 4.5 Ergodic Processes
    6. 4.6 Transmission of a Weakly Stationary Process through a Linear Time-invariant Filter
    7. 4.7 Power Spectral Density of a Weakly Stationary Process
    8. 4.8 Another Definition of the Power Spectral Density
    9. 4.9 Cross-spectral Densities
    10. 4.10 The Poisson Process
    11. 4.11 The Gaussian Process
    12. 4.12 Noise
    13. 4.13 Narrowband Noise
    14. 4.14 Sine Wave Plus Narrowband Noise
    15. 4.15 Summary and Discussion
  11. 5 Information Theory
    1. 5.1 Introduction
    2. 5.2 Entropy
    3. 5.3 Source-coding Theorem
    4. 5.4 Lossless Data Compression Algorithms
    5. 5.5 Discrete Memoryless Channels
    6. 5.6 Mutual Information
    7. 5.7 Channel Capacity
    8. 5.8 Channel-coding Theorem
    9. 5.9 Differential Entropy and Mutual Information for Continuous Random Ensembles
    10. 5.10 Information Capacity Law
    11. 5.11 Implications of the Information Capacity Law
    12. 5.12 Information Capacity of Colored Noisy Channel
    13. 5.13 Rate Distortion Theory
    14. 5.14 Summary and Discussion
  12. 6 Conversion of Analog Waveforms into Coded Pulses
    1. 6.1 Introduction
    2. 6.2 Sampling Theory
    3. 6.3 Pulse-Amplitude Modulation
    4. 6.4 Quantization and its Statistical Characterization
    5. 6.5 Pulse-Code Modulation
    6. 6.6 Noise Considerations in PCM Systems
    7. 6.7 Prediction-Error Filtering for Redundancy Reduction
    8. 6.8 Differential Pulse-Code Modulation
    9. 6.9 Delta Modulation
    10. 6.10 Line Codes
    11. 6.11 Summary and Discussion
  13. 7 Signaling over AWGN Channels
    1. 7.1 Introduction
    2. 7.2 Geometric Representation of Signals
    3. 7.3 Conversion of the Continuous AWGN Channel into a Vector Channel
    4. 7.4 Optimum Receivers Using Coherent Detection
    5. 7.5 Probability of Error
    6. 7.6 Phase-Shift Keying Techniques Using Coherent Detection
    7. 7.7 M-ary Quadrature Amplitude Modulation
    8. 7.8 Frequency-Shift Keying Techniques Using Coherent Detection
    9. 7.9 Comparison of M-ary PSK and M-ary FSK from an Information-Theoretic Viewpoint
    10. 7.10 Detection of Signals with Unknown Phase
    11. 7.11 Noncoherent Orthogonal Modulation Techniques
    12. 7.12 Binary Frequency-Shift Keying Using Noncoherent Detection
    13. 7.13 Differential Phase-Shift Keying
    14. 7.14 BER Comparison of Signaling Schemes over AWGN Channels
    15. 7.15 Synchronization
    16. 7.16 Recursive Maximum Likelihood Estimation for Synchronization
    17. 7.17 Summary and Discussion
  14. 8 Signaling over Band-Limited Channels
    1. 8.1 Introduction
    2. 8.2 Error Rate Due to Channel Noise in a Matched-Filter Receiver
    3. 8.3 Intersymbol Interference
    4. 8.4 Signal Design for Zero ISI
    5. 8.5 Ideal Nyquist Pulse for Distortionless Baseband Data Transmission
    6. 8.6 Raised-Cosine Spectrum
    7. 8.7 Square-Root Raised-Cosine Spectrum
    8. 8.8 Post-Processing Techniques: The Eye Pattern
    9. 8.9 Adaptive Equalization
    10. 8.10 Broadband Backbone Data Network: Signaling over Multiple Baseband Channels
    11. 8.11 Digital Subscriber Lines
    12. 8.12 Capacity of AWGN Channel Revisited
    13. 8.13 Partitioning Continuous-Time Channel into a Set of Subchannels
    14. 8.14 Water-Filling Interpretation of the Constrained Optimization Problem
    15. 8.15 DMT System Using Discrete Fourier Transform
    16. 8.16 Summary and Discussion
  15. 9 Signaling over Fading Channels
    1. 9.1 Introduction
    2. 9.2 Propagation Effects
    3. 9.3 Jakes Model
    4. 9.4 Statistical Characterization of Wideband Wireless Channels
    5. 9.5 FIR Modeling of Doubly Spread Channels
    6. 9.6 Comparison of Modulation Schemes: Effects of Flat Fading
    7. 9.7 Diversity Techniques
    8. 9.8 “Space Diversity-on-Receive” Systems
    9. 9.9 “Space Diversity-on-Transmit” Systems
    10. 9.10 “Multiple-Input, Multiple-Output” Systems: Basic Considerations
    11. 9.11 MIMO Capacity for Channel Known at the Receiver
    12. 9.12 Orthogonal Frequency Division Multiplexing
    13. 9.13 Spread Spectrum Signals
    14. 9.14 Code-Division Multiple Access
    15. 9.15 The RAKE Receiver and Multipath Diversity
    16. 9.16 Summary and Discussion
  16. 10 Error-Control Coding
    1. 10.1 Introduction
    2. 10.2 Error Control Using Forward Error Correction
    3. 10.3 Discrete Memoryless Channels
    4. 10.4 Linear Block Codes
    5. 10.5 Cyclic Codes
    6. 10.6 Convolutional Codes
    7. 10.7 Optimum Decoding of Convolutional Codes
    8. 10.8 Maximum Likelihood Decoding of Convolutional Codes
    9. 10.9 Maximum a Posteriori Probability Decoding of Convolutional Codes
    10. 10.10 Illustrative Procedure for Map Decoding in the Log-Domain
    11. 10.11 New Generation of Probabilistic Compound Codes
    12. 10.12 Turbo Codes
    13. 10.13 EXIT Charts
    14. 10.14 Low-Density Parity-Check Codes
    15. 10.15 Trellis-Coded Modulation
    16. 10.16 Turbo Decoding of Serial Concatenated Codes
    17. 10.17 Summary and Discussion
  17. A Advanced Probabilistic Models
    1. A.1 The Chi-Square Distribution
    2. A.2 The Log-Normal Distribution
    3. A.3 The Nakagami Distribution
  18. B Bounds on the Q-Function
  19. C Bessel Functions
    1. C.1 Series Solution of Bessel’s Equation
    2. C.2 Properties of the Bessel Function
    3. C.3 Modified Bessel Function
  20. D Method of Lagrange Multipliers
    1. D.1 Optimization Involving a Single Equality Constraint
  21. E Information Capacity of MIMO Channels
    1. E.1 Log-Det Capacity Formula of MIMO Channels
    2. E.2 MIMO Capacity for Channel Known at the Transmitter
  22. F Interleaving
    1. F.1 Block Interleaving
    2. F.2 Convolutional Interleaving
    3. F.3 Random Interleaving
  23. G The Peak-Power Reduction Problem in OFDM
    1. G.1 PAPR Properties of OFDM Signals
    2. G.2 Maximum PAPR in OFDM Using M-ary PSK
    3. G.3 Clipping-Filtering: A Technique for PAPR Reduction
  24. H Nonlinear Solid-State Power Amplifiers
    1. H.1 Power Amplifier Nonlinearities
    2. H.2 Nonlinear Modeling of Band-Pass Power Amplifiers
  25. I Monte Carlo Integration
  26. J Maximal-Length Sequences
    1. J.1 Properties of Maximal-Length Sequences
    2. J.2 Choosing a Maximal-Length Sequence
  27. K Mathematical Tables
  28. Glossary
  29. Bibliography
  30. Index
  31. Credits