数字通信基础(英文版) (美)乌帕马尼亚·麦德豪 著 专业科技 文轩网

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
……
8. Wireless communication
Appendix A Probability, random variables and random processes
Appendix B The Chernoff bound
Appendix C Jensen's inequality
References
Index