《现代通信系统(英文版)》(作者莫西亚)是通信系统领域的经典教材，全面介绍了模拟通信系统和数字通信系统以及构成目前光纤、无线和卫星通信网基础设施的基本原理。书中列举了数字有线电视、无线通信、蜂窝通信和网络通信等众多应用实例，并结合这些实例详细分析了信源编码、信道编码、调制/解调、复用与同步技术、基带技术和抗噪技术。

《现代通信系统(英文版)》(作者莫西亚)共分15章，全面介绍了模拟通信系统和数字通信系统，以及构成目前光纤、无线和卫星通信网基础设施的基本原理。书中列举了数字有线电视、无线通信、蜂窝通信和网络通信等众多应用实例，并结合这些实例详细分析了信源编码、信道编码、调制／解调、复用与同步技术、基带技术和抗噪技术。

《现代通信系统(英文版)》可作为高等院校通信类、信息类、电子类、计算机类等专业的研究生或高年级本科生的教材，也可供有关的科研和管理人员参考。

Preface

CHAPTER

Introduction

1.1 Elements of a Communication System

1.2 Communication Channels

1.2.1 Coaxial Cable

1.2.2 Optical Fibers

1.2.3 Radio Channels

1.3 Analog and Digital Communication Systems

1.3.1 Digital Communication Systems

1.3.2 Why Digital Transmission?

1.4 History of Communications

1.4.1 Wireless Communications

1.5 Key Themes and Drivers

Final Remarks

Further Readings

CHAPTER 2

Review of Signals and Linear Systems

2.1 Basic Signal Concepts

2.1.1 Some Useful Basic Signals

2.1.2 Energy and Power Signals

2.1.3 Logarithmic Power Calculations

2.1.4 Some Basic Operations on Signals

2.2 Basic System Concepts

2.2.1 Classification of Systems

2.2.2 Characterization of LTI Systems

2.3 Frequency Domain Representation

2.4 Fourier Series

2.4.1 Trigonometric Fourier Series

2.4.2 Parseval’s Theorem

2.4.3 Convergence of Fourier Series

2.5 Fourier Transform

2.5.1 Fourier Transforms of Some Common Signals

2.5.2 Properties of Fourier Transform

2.5.3 Fourier Transforms of Periodic Signals

2.6 Time-Bandwidth Product

2.7 Transmission of Signals Through LTI Systems

2.7.1 Distortionless Transmission

2.8 LTI Systems as Frequency Selective Filters

2.8.1 Ideal Filters

2.8.2 Realizable Approximations to Ideal Filters

2.8.3 Analog Filter Design Using MATLAB

2.9 Power Spectral Density

2.9.1 Time-Average Autocorrelation Function

2.9.2 Relationship Between Input and Output Power Spectral Densities

2.10 Frequency Response Characteristics of Transmission Media

2.10.1 Twisted Wire Pairs

2.10.2 Coaxial Cable

2.11 Fourier Transforms for Discrete-Time Signals

Final Remarks

Further Readings

Problems

MATLAB Problems

CHAPTER 3

Simulation of Communication Systems Using MATLAB/Simulink

3.1 Getting Started in Simulink

3.1.1 Solvers

3.2 Modeling in Simulink

3.2.1 Subsystems

3.3 Simulation of Signal and Noise Sources

3.3.1 Deterministic Signals

3.3.2 Random Signals

3.3.3 Modeling of AWGN Channel

3.4 Modeling of Communication Systems

3.4.1 Time-Domain Modeling

3.4.2 Transform-Domain Description

3.5 Displaying Signals in Frequency Domain

3.6 Using Simulink with MATLAB

3.6.1 Running Simulations from MATLAB

Final Remarks

Further Readings

CHAPTER 4

Amplitude Modulation

4.1 Low-Pass and Bandpass Signals

4.2 Double-Sideband Suppressed-Carrier AM

4.2.1 Spectrum of the DSB-SC AM Signal

4.2.2 Demodulation of DSB-SC AM Signals

Experiment 4.1 DSB-SC AM Modulation and Demodulation

4.3 Conventional Amplitude Modulation

4.3.1 Spectrum of the Conventional AM Signal

4.3.2 Demodulation of Conventional AM Signal

Experiment 4.2 Conventional AM Modulation and Demodulation

4.4 Alternative Representations for BP Signals and Systems

4.4.1 Frequency Spectrum of Complex Envelope and Analytic Representations

4.4.2 Complex Envelope Representation of BP Systems

4.5 Single-Sideband AM

4.5.1 Demodulation of SSB-AM Signals

Experiment 4.3 SSB-AM Modulation and Demodulation

4.6 Vestigial-Sideband AM

4.7 Quadrature Multiplexing

4.8 Multiplexing

4.8.1 Frequency Division Multiplexing

4.9 Frequency Translation and Selection

4.9.1 Down-Conversion Mixer

4.9.2 Image-Reject Mixers

4.10 Communication Receivers

4.10.1 Superheterodyne Receivers

4.10.2 Direct-Conversion Receivers

4.10.3 Low-IF Receiver Architectures

Final Remarks

Further Readings

Problems

MATLAB Problems

APPENDIX 4A: Hilbert Transform

CHAPTER 5

Angle Modulation

5.1 FM and PM Signals

5.1.1 FM and PM Signals with Sinusoidal Modulating Signal

5.1.2 Power in Angle-Modulated Signal

5.2 Spectrum of Angle-Modulated Signals

5.2.1 Bandwidth of a Sinusoidally Modulated FM Signal

5.2.2 Bandwidth of an FM Signal Modulated by Arbitrary Message Signal

5.3 Narrowband FM

5.4 Demodulation of Angle-Modulated Signals

5.4.1 Bandpass Limiter

5.4.2 Frequency Discriminator

Experiment 5.1 Simulink Model of an FM System with Frequency Discriminator

Experiment 5.2 FM Demodulation with Balanced Slope Detector

5.4.3 Phase-shift Discriminator: Quadrature Detector

5.5 Phase-Locked Loop

5.5.1 Analog Phase-Locked Loop

5.5.2 APLL Linear Model

5.5.3 First-Order PLL

Experiment 5.3 First-Order PLL

5.5.4 Second-Order PLL

Experiment 5.4 Second-Order PLL

5.5.5 Acquisition Process: APLL in the Unlocked State

5.6 PLL as FM Demodulator

Experiment 5.5 PLL as FM Demodulator

5.7 FM Broadcasting

5.7.1 FM Stereo

5.8 Analog Television

5.8.1 Black-and-White Image

5.8.2 Black-and-White Television

5.8.3 Color Television

5.8.4 Multichannel Television Sound

Final Remarks

Further Readings

Problems

MATLAB Problems

CHAPTER 6

Probability and Random Processes

6.1 Probability Concepts

6.1.1 Relative Frequency

6.1.2 Probability Axioms

6.1.3 Union Bound

6.1.4 Conditional Probability

6.2 Random Variables

6.2.1 Discrete Random Variables

6.2.2 Some Common Discrete Random Variables

6.3 Continuous Random Variables

6.3.1 Some Common Continuous Random Variables

6.3.2 PDFs for Discrete and Mixed Random Variables

6.4 Functions of a Random Variable

6.4.1 Case I: g ( x ) Monotonically Increasing or Decreasing

6.4.2 Case II: Arbitrary g ( x )

6.5 Statistics of Random Variables

6.5.1 Moments and Characteristic Functions

6.6 Pairs of Random Variables

6.6.1 Marginal Distributions

6.6.2 Function of Two Random Variables: Expected Values

6.7 Conditional Distributions

6.7.1 Conditional Expected Values

6.7.2 Independent Random Variables

6.8 Jointly Gaussian Random Variables

6.8.1 Two Functions of Two Random Variables

6.8.2 Central Limit Theorem

6.9 Random Processes: Introduction

6.9.1 Characterization of a Random Process

6.9.2 Stationary Random Processes

6.9.3 Wide-Sense Stationary Random Processes

6.9.4 Ergodic Random Processes

6.9.5 Properties of the Autocorrelation Function

6.9.6 Uncorrelated, Orthogonal, and Independent Random Processes

6.10 Power Spectrum of a Random Process

6.10.1 Wiener-Khinchin Theorem

6.10.2 Transmission of Random Signals Through Linear Time-Invariant Systems

6.11 Some Important Random Processes

6.11.1 Gaussian Random Process

6.11.2 White Gaussian Noise

6.11.3 Filtered White Gaussian Noise

6.12 Narrowband Noise

6.12.1 Narrowband White Gaussian Noise

6.12.2 Envelope of Sine Wave in Narrowband Noise

6.13 Noise Sources in Communication Systems

6.13.1 Thermal Noise

6.13.2 Available Power

6.13.3 Shot Noise

6.14 Characterization of System Noise

6.14.1 Noise Factor and Noise Figure

6.14.2 Effective Input Noise Temperature of a Subsystem

6.14.3 Noise Figure of a Cascade of Subsystems

6.14.4 Noise Factor of a Lossy Two-Port Network

6.15 MATLAB Simulation of Random Processes

6.15.1 Generating Arbitrary PDF Random Variables

6.15.2 Autocorrelation Function and Spectral Density

6.15.3 Samples of White Gaussian Noise

Final Remarks

Further Readings

Problems

MATLAB Problems

CHAPTER 7

Noise Performance of Analog Communication Systems

7.1 Noise Performance of Baseband Systems

7.2 Effect of Noise on the Performance of AM Systems

7.2.1 Noise Performance of DSB-SC

Experiment 7.1 Noise Performance of a DSB-SC AM System

7.2.2 Noise Performance of SSB-AM

Experiment 7.2 Noise Performance of an SSB-AM System

7.2.3 Noise Performance of Conventional AM

Experiment 7.3 Noise Performance of Conventional AM System

7.3 Noise Performance of Angle-Modulation Systems

7.3.1 High-R Operation

7.3.2 FM System Operation: Low-R Case

Experiment 7.4 Noise Performance of an FM System

7.4 Preemphasis and Deemphasis

7.5 Comparison of Analog Modulation Systems

7.6 Link Design

7.6.1 Analog Repeater

7.6.2 Performance of Analog Communication System Using Cascade of Repeaters

Final Remarks

Further Readings

Problems

MATLAB Problems

CHAPTER 8

Conversion of Analog Signals to Digital Format

8.1 Sampling of Low-Pass Signals

8.1.1 Nyquist-Shannon Sampling Theorem

8.1.2 DFT of the Sampled Sequence

8.1.3 Reconstruction of the Analog Signal

8.1.4 Practical Sampling Techniques

8.2 Aliasing

Experiment 8.1 Natural Sampling of a LP Random Signal

8.3 Digitization of Analog Signals

8.3.1 Quantization

8.3.2 Coding of Quantized Samples

8.3.3 Errors Introduced by Quantization Process

Experiment 8.2 Study of m-Bit Quantization Errors

8.3.4 Quantization Noise

8.4 Pulse Code Modulation

8.4.1 Nonuniform Quantization

8.5 Differential Pulse Code Modulation

8.6 Oversampling in Analog-to-Digital Conversion

8.7 Delta Modulation

8.7.1 Slope Overload and Granular Noise

8.7.2 Adaptive Delta Modulation

8.7.3 Continuously Variable Slope Delta Modulation

8.7.4 Quantization Noise

Experiment 8.3 Delta Modulation

8.8 Sigma-Delta Modulation

8.8.1 First-Order Sigma-Delta Modulation

8.8.2 Noise Performance

Experiment 8.4 Sigma-Delta Modulation

8.9 Sampling Theorem for Bandpass Signals

Experiment 8.5 Natural Sampling of a BP Random Signal

8.9.1 BP Sampling in Digital Receivers

Final Remarks

Further Readings

Problems

MATLAB Problems

CHAPTER 9

Digital Baseband Modulation

9.1 Pulse Amplitude Modulation

9.2 Binary Line-Coding Techniques

9.3 Spectra of Digital Baseband Signals

9.3.1 Power Spectral Density of Random Pulse Trains

9.3.2 Spectra of Binary Line Codes

Experiment 9.1 Waveforms and Spectra of Several Line-Coding Schemes

9.4 Bandwidth of Digital Baseband Signals

9.5 Spectral and Power Out-of-Band Plots

9.6 Block Line Codes

9.6.1 Binary Block Codes

9.6.2 Multilevel Block Codes

9.7 Scrambling

9.7.1 Frame-Synchronous Scrambler

9.7.2 SONET Scrambler

9.7.3 Self-Synchronous Scrambler

9.7.4 ATM Scrambler

9.8 Pulse Shaping to Improve Spectral Efficiency

9.8.1 Sinc Pulse

9.8.2 Raised Cosine Pulses

Experiment 9.2 Effect of Channel on Baseband Digital Signals

9.9 Estimation of Allowable Bit Rate

Final Remarks

Further Readings

Problems

MATLAB Problems

CHAPTER 10

Detection of Baseband Signals in Noise

10.1 Binary Signal Detection in AWGN

10.1.1 Probability of Bit Error

10.2 The Matched Filter

10.2.1 Correlation Detectors

10.2.2 Performance of Binary Signaling Systems

Experiment 10.1 Binary Antipodal System with Correlation Detector

Experiment 10.2 Binary Antipodal Signaling System with Matched-Filter Detection

10.3 Vector Space Concepts

10.3.1 Finite Dimensional Vector Spaces

10.3.2 Inner-Product Vector Spaces

10.3.3 Gram-Schmidt Orthonormalization Procedure

10.4 Vector Space Representation of Signals and WGN

10.4.1 Vector Space Representation of Waveforms

10.4.2 Examples of Signal Constellations

10.4.3 Vector Space Representation of WGN

10.5 M -ary Signal Detection in AWGN

10.5.1 The Maximum a Posteriori Detector

10.5.2 The Maximum Likelihood Detector

10.5.3 MAP and ML Detector Implementations

10.5.4 Decision Regions

10.6 Error Performance of ML Detectors

10.6.1 Two-Signal Error Probability

10.6.2 M -Signal Error Probability

10.6.3 Relationship Between Bit and Symbol Error Rates

10.7 Error Performance of M -ary PAM Signals

Experiment 10.3 Noise Performance of 4-PAM Signaling System

Final Remarks

Further Readings

Problems

MATLAB Problems

CHAPTER 11

Digital Information Transmission Using Carrier Modulation

11.1 Basic Concepts

11.1.1 Representations of Digitally Modulated Carrier Signals

11.2 Binary Amplitude-Shift Keying

11.2.1 Coherent Demodulation of BASK Signals

Experiment 11.1 BASK Simulation and Performance Comparison

11.3 Binary Phase-Shift Keying

11.3.1 Coherent Demodulation of BPSK Signals

Experiment 11.2 BPSK Simulation and Performance Comparison

11.4 Binary Frequency-Shift Keying

11.4.1 Orthogonality of BFSK Signals

11.4.2 Coherent Demodulation of BFSK Signals

Experiment 11.3 BFSK Simulation and Performance Comparison

11.5 Differential Binary Phase-Shift Keying

11.6 Noncoherent Demodulation of Binary Digital Carrier Signals

11.6.1 Noncoherent Binary ASK

11.6.2 Noncoherent Binary FSK

11.7 Quadrature Modulation Schemes

11.7.1 Demodulation of Quadrature-Modulated Signals

11.7.2 QPSK

Experiment 11.4 QPSK Simulation and Performance Comparison

11.7.3 Offset QPSK

Experiment 11.5 OQPSK Simulation and Performance Comparison

11.7.4 M -ary Phase-Shift Keying

11.8 Minimum Shift Keying

Experiment 11.6 MSK Simulation and Performance Comparison

11.9 Quadrature Amplitude Modulation

Experiment 11.7 16-QAM System Simulation and Performance Comparison

11.10 Spectra of Quadrature Modulated Signals

11.10.1 Other Bandwidth Definitions

11.11 Comparison of Carrier Modulation Schemes

Final Remarks

Further Readings

Problems

MATLAB Problems

CHAPTER 12

Digital Signal Transmission Through Time Dispersive Channels

12.1 Transmission of PAM Signals Through Bandlimited Channels

12.1.1 Eye Diagrams

12.2 Nyquist’s Criterion for Zero ISI

12.2.1 RC Pulse Signaling

12.3 Transmit and Receive Filters for Bandlimited AWGN Channels

12.3.1 Probability of Error Performance

12.4 Partial Response (Duobinary) Signaling

12.4.1 Detection of Duobinary Signals

12.4.2 Probability of Error Performance

12.5 Linear Equalizers

12.5.1 Zero-Forcing Equalizer

12.5.2 Minimum Mean-Square Error Equalizer

12.6 Adaptive Equalization

12.6.1 Least Mean Square Error Algorithm

12.7 Decision Feedback Equalizers

12.7.1 Coefficient Optimization

12.7.2 Channel Estimation

12.8 Performance of Linear and Decision Feedback Equalizers

Final Remarks

Further Readings

Problems

MATLAB Problems

CHAPTER 13

Digital Multiplexing and Synchronization

13.1 Digital Multiplexing

13.1.1 Plesiochronous Digital Hierarchies

13.1.2 Synchronization of PDH Signals

13.1.3 M12 Multiplexer: DS2 Frame

13.1.4 DS2 OH Bits

13.2 SONET

13.2.1 Multiplexing of SONET Signals

13.2.2 Synchronization of SONET Signals

13.3 Carrier Synchronization

13.3.1 Raised-Power Loops

13.3.2 Costas Loop

13.3.3 Effect of Noise on the Carrier Phase Estimation

13.3.4 Effect of Noise on the Performance of Carrier Synchronizers

13.4 Symbol Synchronization

13.4.1 Clock Recovery from NRZ Data

13.4.2 PLL for Clock Recovery

Experiment 13.1 SONET OC-48 Clock and Data Recovery Using PLL

13.5 Frame Synchronization

13.5.1 Performance of a Frame Synchronizer

13.5.2 Choice of Frame Alignment Word

Final Remarks

Further Readings

Problems

MATLAB Problems

CHAPTER 14

Information Theory and Compression Techniques

14.1 Basic Concepts of Information Theory

14.1.1 Joint and Conditional Entropy

14.1.2 Differential Entropy

14.1.3 Mutual Information

14.2 Source Coding

14.2.1 Discrete Memoryless Sources

14.2.2 Shannon’s Source Coding Theorem

14.3 Channel Coding

14.3.1 Modeling of Communication Channels

14.3.2 Capacity of a Communication Channel

14.3.3 Shannon’s Channel Capacity Theorem

14.3.4 Another Channel Coding Theorem

14.4 Capacity of AWGN Channels

14.4.1 Shannon’s Capacity Theorem for AWGN Channels

14.4.2 Capacity of Bandlimited AWGN Channels

14.4.3 Implications of Capacity Theorem for Bandlimited AWGN Channels

14.4.4 Power-Bandwidth Trade-Offs

14.5 Lossless Compression Techniques

14.5.1 Lossless Compression Techniques

14.5.2 Huffman Coding

14.5.3 Run-Length Encoding

14.5.4 Lempel-Ziv Coding

14.6 Image Compression: JPEG

14.6.1 Discrete Cosine Transform

14.6.2 JPEG Compression Standard

14.6.3 Subsampling of Chrominance Components

14.7 Digital Video Compression: MPEG

14.7.1 MPEG

Final Remarks

Further Readings

Problems

MATLAB Problems

APPENDIX A: Capacity of AWGN Channel: Alternative Proof

CHAPTER 15

Channel Coding Techniques

15.1 Block Codes

15.1.1 Linear Block Codes

15.1.2 Systematic Linear Block Codes

15.1.3 Error and Syndrome Vectors

15.2 Hard-Decision Decoding of Block Codes

15.2.1 Syndrome Decoding of Block Codes

15.2.2 Error-Detecting and Error-Correcting Capabilities

15.3 Cyclic Codes

15.3.1 Encoding of Systematic Cyclic Codes

15.3.2 Decoding of Cyclic Codes

15.3.3 Important Families of Block Codes

15.3.4 Cyclic Redundancy Check Codes

15.4 Error Correction Performance of Hard-Decision Decoded Block Codes

15.5 Soft-Decision Decoding of Block Codes

15.5.1 Soft-Decision Decoding Error Performance

15.5.2 Coding Gain

15.6 Convolutional Codes

15.6.1 Representation of Convolutional Codes

15.6.2 Decoding of Convolutional Codes

15.6.3 The Viterbi Algorithm

15.7 Error Performance of Convolutional Codes

15.7.1 Transfer Function of a Convolutional Code

15.7.2 Probability of Error for Convolutional Codes

15.7.3 Coding Gain

15.8 Turbo Codes

15.8.1 Turbo Decoding

15.8.2 Performance of Turbo Codes

15.9 Trellis-Coded Modulation

15.9.1 Decoding of TCM Codes

Final Remarks

Further Readings

Problems

MATLAB Problems

APPENDIX A

Mathematical Tables

APPENDIX B

Abbreviations

APPENDIX C

List of Symbols

Index