圖書內容
本書是通信系統領域的經典教材，全面介紹了模擬通信系統和數字通信系統以及構成目前光纖、無線和衛星通信網基礎設施的基本原理。書中列舉了數字有線電視、無線通信、蜂窩通信和網路通信等眾多套用實例，並結合這些實例詳細分析了信源編碼、信道編碼、調製/解調、復用與同步技術、基帶技術和抗噪技術。
目錄
Preface xv
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-CNR Operation
7.3.2 FM System Operation: Low-CNR 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
