数字通信原理 英文版2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载

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1 Introduction to digital communication1

1.1 Standardized interfaces and layering3

1.2 Communication sources5

1.2.1 Source coding6

1.3 Communication channels7

1.3.1 Channel encoding(modulation)10

1.3.2 Error correction11

1.4 Digital interface12

1.4.1 Network aspects of the digital interface12

1.5 Supplementary reading14

2 Coding for discrete sources16

2.1 Introduction16

2.2 Fixed-length codes for discrete sources18

2.3 Variable-length codes for discrete sources19

2.3.1 Unique decodability20

2.3.2 Prefix-free codes for discrete sources21

2.3.3 The Kraft inequality for prefix-free codes23

2.4 Probability models for discrete sources26

2.4.1 Discrete memoryless sources26

2.5 Minimum ？ for prefix-free codes27

2.5.1 Lagrange multiplier solution for the minimum ？28

2.5.2 Entropy bounds on ？29

2.5.3 Huffman's algorithm for optimal source codes31

2.6 Entropy and fixed-to-variable-length codes35

2.6.1 Fixed-to-variable-length codes37

2.7 The AEP and the source coding theorems38

2.7.1 The weak law of large numbers39

2.7.2 The asymptotic equipartition property40

2.7.3 Source coding theorems43

2.7.4 The entropy bound for general classes of codes44

2.8 Markov sources46

2.8.1 Coding for Markov sources48

2.8.2 Conditional entropy48

2.9 Lempel-Ziv universal data compression51

2.9.1 The LZ77 algorithm51

2.9.2 Why LZ77 works53

2.9.3 Discussion54

2.10 Summary of discrete source coding55

2.11 Exercises56

3 Quantization67

3.1 Introduction to quantization67

3.2 Scalar quantization68

3.2.1 Choice of intervals for given representation points69

3.2.2 Choice of representation points for given intervals69

3.2.3 The Lloyd-Max algorithm70

3.3 Vector quantization72

3.4 Entropy-coded quantization73

3.5 High-rate entropy-coded quantization75

3.6 Differential entropy76

3.7 Performance of uniform high-rate scalar quantizers78

3.8 High-rate two-dimensional quantizers81

3.9 Summary of quantization84

3.10 Appendixes85

3.10.1 Nonuniform scalar quantizers85

3.10.2 Nonuniform 2D quantizers87

3.11 Exercises88

4 Source and channel waveforms93

4.1 Introduction93

4.1.1 Analog sources93

4.1.2 Communication channels95

4.2 Fourier series96

4.2.1 Finite-energy waveforms98

4.3 ？2 functions and Lebesgue integration over[-T/2,T/2]101

4.3.1 Lebesgue measure for a union of intervals102

4.3.2 Measure for more general sets104

4.3.3 Measurable functions and integration over[-T/2,T/2]106

4.3.4 Measurability of functions defined by other functions108

4.3.5 ？1 and ？2 functions over[-T/2,T/2]108

4.4 Fourier series for ？2 waveforms109

4.4.1 The T-spaced truncated sinusoid expansion111

4.5 Fourier transforms and ？2 waveforms114

4.5.1 Measure and integration over R116

4.5.2 Fourier transforms of ？2 functions118

4.6 The DTFT and the sampling theorem120

4.6.1 The discrete-time Fourier transform121

4.6.2 The sampling theorem122

4.6.3 Source coding using sampled waveforms124

4.6.4 The sampling theorem for[△-W,△+W]125

4.7 Aliasing and the sinc-weighted sinusoid expansion126

4.7.1 The T-spaced sinc-weighted sinusoid expansion127

4.7.2 Degrees of freedom128

4.7.3 Aliasing-a time-domain approach129

4.7.4 Aliasing-a frequency-domain approach130

4.8 Summary132

4.9 Appendix:Supplementary material and proofs133

4.9.1 Countable sets133

4.9.2 Finite unions of intervals over[-T/2,T/2]135

4.9.3 Countable unions and outer measure over[-T/2,T/2]136

4.9.4 Arbitrary measurable sets over[-T/2,T/2]139

4.10 Exercises143

5 Vector spaces and signal space153

5.1 Axioms and basic properties of vector spaces154

5.1.1 Finite-dimensional vector spaces156

5.2 Inner product spaces158

5.2.1 The inner product spaces Rn and Cn158

5.2.2 One-dimensional projections159

5.2.3 The inner product space of ？2 functions161

5.2.4 Subspaces of inner product spaces162

5.3 Orthonormal bases and the projection theorem163

5.3.1 Finite-dimensional projections164

5.3.2 Corollaries of the projection theorem165

5.3.3 Gram-Schmidt orthonormalization166

5.3.4 Orthonormal expansions in ？2167

5.4 Summary169

5.5 Appendix:Supplementary material and proofs170

5.5.1 The Plancherel theorem170

5.5.2 The sampling and aliasing theorems174

5.5.3 Prolate spheroidal waveforms176

5.6 Exercises177

6 Channels,modulation,and demodulation181

6.1 Introduction181

6.2 Pulse amplitude modulation(PAM)184

6.2.1 Signal constellations184

6.2.2 Channel imperfections:a preliminary view185

6.2.3 Choice of the modulation pulse187

6.2.4 PAM demodulation189

6.3 The Nyquist criterion190

6.3.1 Band-edge symmetry191

6.3.2 Choosing{p(t-kT);k∈Z}as an orthonormal set193

6.3.3 Relation between PAM and analog source coding194

6.4 Modulation:baseband to passband and back195

6.4.1 Double-sideband amplitude modulation195

6.5 Quadrature amplitude modulation(QAM)196

6.5.1 QAM signal set198

6.5.2 QAM baseband modulation and demodulation199

6.5.3 QAM:baseband to passband and back200

6.5.4 Implementation of QAM201

6.6 Signal space and degrees of freedom203

6.6.1 Distance and orthogonality204

6.7 Carrier and phase recovery in QAM systems206

6.7.1 Tracking phase in the presence of noise207

6.7.2 Large phase errors208

6.8 Summary of modulation and demodulation208

6.9 Exercises209

7 Random processes and noise216

7.1 Introduction216

7.2 Random processes217

7.2.1 Examples of random processes218

7.2.2 The mean and covariance of a random process220

7.2.3 Additive noise channels221

7.3 Gaussian random variables,vectors,and processes221

7.3.1 The covariance matrix of a jointly Gaussian random vector224

7.3.2 The probability density of a jointly Gaussian random vector224

7.3.3 Special case of a 2D zero-mean Gaussian random vector227

7.3.4 Z=AW,where A is orthogonal228

7.3.5 Probability density for Gaussian vectors in terms of principal axes228

7.3.6 Fourier transforms for joint densities230

7.4 Linear functionals and filters for random processes231

7.4.1 Gaussian processes defined over orthonormal expansions232

7.4.2 Linear filtering of Gaussian processes233

7.4.3 Covariance for linear functionals and filters234

7.5 Stationarity and related concepts235

7.5.1 Wide-sense stationary(WSS)random processes236

7.5.2 Effectively stationary and effectively WSS random processes238

7.5.3 Linear functionals for effectively WSS random processes239

7.5.4 Linear filters for effectively WSS random processes239

7.6 Stationarity in the frequency domain242

7.7 White Gaussian noise244

7.7.1 The sinc expansion as an approximation to WGN246

7.7.2 Poisson process noise247

7.8 Adding noise to modulated communication248

7.8.1 Complex Gaussian random variables and vectors250

7.9 Signal-to-noise ratio251

7.10 Summary of random processes254

7.11 Appendix:Supplementary topics255

7.11.1 Properties of covariance matrices255

7.11.2 The Fourier series expansion of a truncated random process257

7.11.3 Uncorrelated coefficients in a Fourier series259

7.11.4 The Karhunen-Loeve expansion262

7.12 Exercises263

8 Detection,coding,and decoding268

8.1 Introduction268

8.2 Binary detection271

8.3 Binary signals in white Gaussian noise273

8.3.1 Detection for PAM antipodal signals273

8.3.2 Detection for binary nonantipodal signals275

8.3.3 Detection for binary real vectors in WGN276

8.3.4 Detection for binary complex vectors in WGN279

8.3.5 Detection of binary antipodal waveforms in WGN281

8.4 M-ary detection and sequence detection285

8.4.1 M-ary detection285

8.4.2 Successive transmissions of QAM signals in WGN286

8.4.3 Detection with arbitrary modulation schemes289

8.5 Orthogonal signal sets and simple channel coding292

8.5.1 Simplex signal sets293

8.5.2 Biorthogonal signal sets294

8.5.3 Error probability for orthogonal signal sets294

8.6 Block coding298

8.6.1 Binary orthogonal codes and Hadamard matrices298

8.6.2 Reed-Muller codes300

8.7 Noisy-channel coding theorem302

8.7.1 Discrete memoryless channels303

8.7.2 Capacity304

8.7.3 Converse to the noisy-channel coding theorem306

8.7.4 Noisy-channel coding theorem,forward part307

8.7.5 The noisy-channel coding theorem for WGN311

8.8 Convolutional codes312

8.8.1 Decoding of convolutional codes314

8.8.2 The Viterbi algorithm315

8.9 Summary of detection,coding,and decoding317

8.10 Appendix:Neyman-Pearson threshold tests317

8.11 Exercises322

9 Wireless digital communication330

9.1 Introduction330

9.2 Physical modeling for wireless channels334

9.2.1 Free-space,fixed transmitting and receiving antennas334

9.2.2 Free-space,moving antenna337

9.2.3 Moving antenna,reflecting wall337

9.2.4 Reflection from a ground plane340

9.2.5 Shadowing340

9.2.6 Moving antenna,multiple reflectors341

9.3 Input/output models of wireless channels341

9.3.1 The system function and impulse response for LTV systems343

9.3.2 Doppler spread and coherence time345

9.3.3 Delay spread and coherence frequency348

9.4 Baseband system functions and impulse responses350

9.4.1 A discrete-time baseband model353

9.5 Statistical channel models355

9.5.1 Passband and baseband noise358

9.6 Data detection359

9.6.1 Binary detection in flat Rayleigh fading360

9.6.2 Noncoherent detection with known channel magnitude363

9.6.3 Noncoherent detection in flat Rician fading365

9.7 Channel measurement367

9.7.1 The use of probing signals to estimate the channel368

9.7.2 Rake receivers373

9.8 Diversity376

9.9 CDMA:the IS95 standard379

9.9.1 Voice compression380

9.9.2 Channel coding and decoding381

9.9.3 Viterbi decoding for fading channels382

9.9.4 Modulation and demodulation383

9.9.5 Multiaccess interference in IS95386

9.10 Summary of wireless communication388

9.11 Appendix:Error probability for noncoherent detection390

9.12 Exercises391

References398

Index400