內容大鋼
本書是一本講授數字通信系統設計基礎概念與原理的簡明教程。書中有大量從簡單到最前沿的實例來展示理論是如何指導實踐的。讀者可以用計算和模擬來實現書中的演算法，由此來理解其背後的理論。本書包含5G通信所使用的Turbo碼和LDPC碼等前沿內容，讀者可以自己編程去實現性能評估與比較。本書還包括空時通信技術和對非相干通信和均衡的幾何解釋等特色內容。本書既可作為通信專業高年級本科生和研究生教材，又可供工程技術人員參考。
作者介紹
(美)烏帕馬尼亞·麥德豪|責編:陳亮//夏丹
烏帕馬尼亞·麥德豪（Upamanyu Madhow）是美國加州大學聖芭芭拉分校電子與電腦工程系的教投。他是三家無線通信初創公司的共同創始人，並持有14項美國專利。麥德豪教授是國際電氣電子工程師學會的傑出會士（IEEE Fellow），擔任過IEEE Transactions on Information Theory, IEEE Transactions on Communications, IEEE Transactions on Information Forensics and Securiy等多家權成期刊的副主編。麥德豪教投還獲得過IEEE無線通信最佳論文獎（IEEE Marconi Prize Paper Award in Wreless Communications），併入選ISI全球高引用科學家名單（ISI HighlyCited Researcher）。
目錄
Preface
Acknowledgements
1 Introduction
1.1 Components of a digital communication system
1.2 Text outline
1.3 Further reading
2 Modulation
2.1 Preliminaries
2.2 Complex baseband representation
2.3 Spectral description of random processes
2.3.1 Complex envelope for passband random processes
2.4 Modulation degrees of freedom
2.5 Linear modulation
2.5.1 Examples of linear modulation
2.5.2 Spectral occupancy of linearly modulated signals
2.5.3 The Nyquist criterion: relating bandwidth to symbol rate
2.5.4 Linear modulation as a building block
2.6 Orthogonal and biorthogonal modulation
2.7 Differential modulation
2.8 Further reading
2.9 Problems
2.9.1 Signals and systems
2.9.2 Complex baseband representation
2.9.3 Random processes
2.9.4 Modulation
3 Demodulation
3.1 Gaussian basics
3.2 Hypothesis testing basics
3.3 Signal space concepts
3.4 Optimal reception in AWGN
3.4.1 Geometry of the ML decision rule
3.4.2 Soft decisions
3.5 Performance analysis of ML reception
3.5.1 Performance with binary signaling
3.5.2 Performance with M-ary signaling
3.6 Bit-level demodulation
3.6.1 Bit-level soft decisions
3.7 Elements of link budget analysis
3.8 Further reading
3.9 Problems
3.9.1 Gaussian basics
3.9.2 Hypothesis testing basics
3.9.3 Receiver design and performance analysis for the AWGN channel
3.9.4 Link budget analysis
3.9.5 Some mathematical derivations
4 Synchronization and noncoherent communication
4.1 Receiver design requirements
4.2 Parameter estimation basics
4.2.1 Likelihood function of a signal in AWGN
4.3 Parameter estimation for synchronization
4.4 Noncoherent communication
4.4.1 Composite hypothesis testing
4.4.2 Optimal noncoherent demodulation
4.4.3 Differential modulation and demodulation
4.5 Performance of noncoherent communieation
4.5 .]Proper complex Gaussianity
4.5.2 Performance of binary noncoherent communication
4.5.3 Performance of M-ary noncoherent orthogonal signaling
4.5.4 Performance of DPSK
4.5.5 Block noncoherent demoxdulation
4.6 Further reading
4.7 Problems
5 Channel equalization
5.1 The channel model
5.2 Receiver front end
5.3 Eye diagrams
5.4 Maximum likelihood sequence estimation
5.4.1 Alternative MLSE formulation
5.5 Geometric model for suboptimal equalizer design
5.6 Linear equalization
5.6.1 Adaptive implementations
5.6.2 Performance analysis
5.7 Decision feedback equalization
5.7.1 Performance analysis
5.8 Performance analysis of MLSE
5.8.1 Union bound
5.8.2 Transfer function bound
5.9 Numerical comparison of equalization techniques
5.10 Further reading
5.11 Problems
5.11.1 MLSE
6 Information-theoretic limits and their computation
6.1 Capacity of AWGN channel: modeling and geometry
6.1.1 From continuous to discrete time
6.1.2 Capacity of the discrete-time AWGN channel
6.1.3 From discrete to continuous time
6.1.4 Summarizing the discrete-time AWGN model
6.2 Shannon theory basics
6.2.1 Entropy, mutual information, and divergence
6.2.2 The channel coding theorem
6.3 Some capacity computations
6.3.1 Capacity for standard constellations
6.3.2 Parallel Gaussian channels and waterfilling
6.4 Optimizing the input distribution
6.4.1 Convex optimization
6.4.2 Characterizing optimal input distributions
6.4.3 Computing optimal input distributions
6.5 Further reading
6.6 Problems
7 Channel coding
7.1 Binary convolutional codes
7.1.1 Nonrecursive nonsystematic encoding
7.1.2 Recursive systematic encoding
7.1.3 Maximum likelihood decoding
7.1.4 Performance analysis of ML decoding
7.1.5 Performance analysis for quantized observations
7.2 Turbo codes and iterative decoding
7.2.1 The BCJR algorithm: soft-in, soft-out decoding
7.2.2 Logarithmic BCJR algorithm
7.2.3 Turbo constructions from convolutional codes
7.2.4 The BER performance of turbo codes
7.2.5 Extrinsic information transfer charts
7.2.6 Turbo weight enumeration
7.3 Low density parity check codes
7.3.1 Some terminology from coding theory
7.3.2 Regular LDPC codes
7.3.3 Irregular LDPC codes
7.3.4 Message passing and density evolution
7.3.5 Belief propagation
7.3.6 Gaussian approximation
7.4 Bandwidth-efficient coded modulation
7.4.1 Bit interleaved coded modulation
7.4.2 Trellis coded modulation
7.5 Algebraic codes
7.6 Further reading
7.7 Problems
8 Wireless communication
8.1 Channel modeling
8.2 Fading and diversity
8.2.1 The problem with Rayleigh fading
8.2.2 Diversity through coding and interleaving
8.2.3 Receive diversity
8.3 Orthogonal frequency division multiplexing
8.4 Direct sequence spread spectrum
8.4.1 The rake receiver
8.4.2 Choice of spreading sequences
8.4.3 Performance of conventional reception in CDMA systems
8.4.4 Multiuser detection for DS-CDMA systems
8.5 Frequency hop spread spectrum
8.6 Continuous phase modulation
8.6.1 Gaussian MSK
8.6.2 Receiver design and Laurent's expansion
8.7 Space-time communication
8.7.1 Space-time channel modeling
8.7.2 Information-theoretic limits
8.7.3 Spatial multiplexing
8.7.4 Space-time coding
8.7.5 Transmit beamforming
8.8 Further reading
8.9 Problems
Appendix A Probability, random variables, and random proceses
A.1 Basic probability
A.2 Random variables
A.3 Random processes
A.3.1 Wide sense stationary random processes through LTI systems
A.3.2 Discrete-time random processes
A.4 Further reading
Appendix B The Chenoff bound
Appendix C Jensen's inequality
References
Index
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