全新正版数字通信基础(英文版)9787519212452世界图书出版公司

Preface
Acknowledgements
1 Introduction
1.1 Components of a digital communication system
1.2 Text outline
1.3 Further reading
2 Moaton
2.1 Preliminaries
2.2 Complex baseband representation
. Spectral description of rando rceses
..1 Complex envelope for passband rando rceses
2.4 Moaton degrees of freedom
2.5 Linear moaton
2.5.1 Examples of linear moaton
2.5.2 Spectral occupancy of linearly modulated signals
2.5.3 The Nyquist criterion: relating bandwidth to symbol rate
2.5.4 Linear moaton as a building block
2.6 Orthogonal and biorthogonal moaton
2.7 Differential moaton
2.8 Further reading
2.9 Problems
2.9.1 Signals and systems
2.9.2 Complex baseband representation
2.9.3 Rando rceses
2.9.4 Moaton
3 Demoaton
3.1 Gaussian basics
3.2 Hypothesis testing basics
3.3 Signal space concepts
3.4 Optima rceto 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 demoaton
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 Coite hypothesis testing
4.4.2 Optimal noncoherent demoaton
4.4.3 Differential moaton and demoaton
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 demoxaton
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 o

乌帕马尼亚·麦德豪（Upamanyu Madhow）是美国加州大学圣芭芭拉分校与计算机工程系的教授。他是三家无线通信初创公司的共同创始人，并持有14项美国。麦德豪教授是靠前气电学会的杰出会士（IEEE Fellow），担任过IEEE Transactions on Information Theory，IEEE Transactions on Communications，IEEE Transactions on Information Forensics and Security等多家不错期刊的副主编。麦德豪教授还获得过IEEE无线通信很好奖（IEEE Marconi Prize Paper Award in Wireless Communications），并入选ISI优选计算机领域高引用科学家名单（ISI Highly Cited Researcher）。