数字通信原理(英文版)(图灵原版电子与电气工程系列)(Principles of Digital Communication)
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品牌: 加拉格(Robert G.Gallager)
基本信息·出版社：人民邮电出版社
·页码：407 页
·出版日期：2010年04月
·ISBN：9787115223364
·条形码：9787115223364
·版本：第1版
·装帧：平装
·开本：16
·正文语种：英语
·丛书名：图灵原版电子与电气工程系列
·外文书名：Principles of Digital Communication
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内容简介本书是世界通信权威、信息领域泰斗Robert G. Gallager博士新作，在数字通信原理的基础上精炼，重点阐述了理论、问题和工程设计之间的关系。内容涉及离散源编码、量化、信道波形、向量空间和信号空间、随机过程和噪声、编码、解码等数字通信基本问题，最后还简单介绍了无线数字通信。 本书是通信专业高年级本科生和研究生教材，也可供工程技术人员参考。
目录
目录 1Introduction to digital communication1 1.1Standardized interfaces and layering3 1.2Communication sources5 1.2.1Source coding6 1.3Communication channels7 1.3.1Channel encoding (modulation)10 1.3.2Error correction11 1.4Digital interface12 1.4.1Network aspects of the digital interface12 1.5Supplementary reading14 2Coding for discrete sources16 2.1Introduction16 2.2Fixed-length codes for discrete sources18 2.3Variable-length codes for discrete sources19 2.3.1Unique decodability20 2.3.2Prefix-free codes for discrete sources21 2.3.3The Kraft inequality for prefix-free codes23 2.4Probability models for discrete sources26 2.4.1Discrete memoryless sources26 2.5Minimum L for prefix-free codes27 2.5.1Lagrange multiplier solution for the minimum L28 2.5.2Entropy bounds on L29 2.5.3Huffman’s algorithm for optimal source codes31 2.6Entropy and fixed-to-variable-length codes35 2.6.1Fixed-to-variable-length codes37 2.7The AEP and the source coding theorems38 2.7.1The weak law of large numbers39 2.7.2The asymptotic equipartition property40 2.7.3Source coding theorems43 2.7.4The entropy bound for general classes of codes44 2.8Markov sources46 2.8.1Coding for Markov sources48 2.8.2Conditional entropy48 2.9Lempel-Ziv universal data compression51 2.9.1The LZ77 algorithm51 2.9.2Why LZ77 works53 2.9.3Discussion54 2.10Summary of discrete source coding55 2.11Exercises56 3Quantization67 3.1Introduction to quantization67 3.2Scalar quantization68 3.2.1Choice of intervals for given representation points69 3.2.2Choice of representation points for given intervals69 3.2.3The Lloyd-Max algorithm70 3.3Vector quantization72 3.4Entropy-coded quantization73 3.5High-rate entropy-coded quantization75 3.6Differential entropy76 3.7Performance of uniform high-rate scalar quantizers78 3.8High-rate two-dimensional quantizers81 3.9Summary of quantization84 3.10Appendixes85 3.10.1Nonuniform scalar quantizers85 3.10.2Nonuniform 2D quantizers87 3.11Exercises88 4Source and channel waveforms93 4.1Introduction93 4.1.1Analog sources93 4.1.2Communication channels95 4.2Fourier series96 4.2.1Finite-energy waveforms98 4.3L2 functions and Lebesgue integration over[-T/2,T/2]101 4.3.1Lebesgue measure for a union of intervals102 4.3.2Measure for more general sets104 4.3.3Measurable functions and integration over [-T/2,T/2]106 4.3.4Measurability of functions defined by other functions108 4.3.5L1 and L2 functions over [-T/2,T/2]108 4.4Fourier series for L2 waveforms109 4.4.1The T-spaced truncated sinusoid expansion111 4.5Fourier transforms and L2 waveforms114 4.5.1Measure and integration over R116 4.5.2Fourier transforms of L2 functions118 4.6The DTFT and the sampling theorem120 4.6.1The discrete-time Fourier transform121 4.6.2The sampling theorem122 4.6.3Source coding using sampled waveforms124 4.6.4The sampling theorem for[Δ-W,Δ+W]125 4.7Aliasing and the sinc-weighted sinusoid expansion126 4.7.1The T-spaced sinc-weighted sinusoid expansion127 4.7.2Degrees of freedom128 4.7.3Aliasing — a time-domain approach129 4.7.4Aliasing — a frequency-domain approach130 4.8Summary132 4.9Appendix: Supplementary material and proofs133 4.9.1Countable sets133 4.9.2Finite unions of intervals over [-T/2,T/2]135 4.9.3Countable unions and outer measure over [-T/2,T/2]136 4.9.4Arbitrary measurable sets over[-T/2,T/2]139 4.10Exercises143 5Vector spaces and signal space153 5.1Axioms and basic properties of vector spaces154 5.1.1Finite-dimensional vector spaces156 5.2Inner product spaces158 5.2.1The inner product spaces Rn and Cn158 5.2.2One-dimensional projections159 5.2.3The inner product space of L2 functions161 5.2.4Subspaces of inner product spaces162 5.3Orthonormal bases and the projection theorem163 5.3.1Finite-dimensional projections164 5.3.2Corollaries of the projection theorem165 5.3.3Gram–Schmidt orthonormalization166 5.3.4Orthonormal expansions in L2167 5.4Summary169 5.5Appendix: Supplementary material and proofs170 5.5.1The Plancherel theorem170 5.5.2The sampling and aliasing theorems174 5.5.3Prolate spheroidal waveforms176 5.6Exercises177 6Channels, modulation, and demodulation181 6.1Introduction181 6.2Pulse amplitude modulation (PAM)184 6.2.1Signal constellations184 6.2.2Channel imperfections: a preliminary view185 6.2.3Choice of the modulation pulse187 6.2.4PAM demodulation189 6.3The Nyquist criterion190 6.3.1Band-edge symmetry191 6.3.2Choosing {p(t-kT);k∈Z} an orthonormal set193 6.3.3Relation between PAM and analog source coding194 6.4Modulation: baseband to passband and back195 6.4.1Double-sideband amplitude modulation195 6.5Quadrature amplitude modulation (QAM)196 6.5.1QAM signal set198 6.5.2QAM baseband modulation and demodulation199 6.5.3QAM: baseband to passband and back200 6.5.4Implementation of QAM201 6.6Signal space and degrees of freedom203 6.6.1Distance and orthogonality204 6.7Carrier and phase recovery in QAM systems206 6.7.1Tracking phase in the presence of noise207 6.7.2Large phase errors208 6.8Summary of modulation and demodulation208 6.9Exercises209 7Random processes and noise216 7.1Introduction216 7.2Random processes217 7.2.1Examples of random processes218 7.2.2The mean and covariance of a random process220 7.2.3Additive noise channels221 7.3Gaussian random variables, vectors, and processes221 7.3.1The covariance matrix of a jointly Gaussianrandom vector224 7.3.2The probability density of a jointly Gaussianrandom vector224 7.3.3Special case of a 2D zero-mean Gaussian random vector227 7.3.4Z=AW, where A is orthogonal228 7.3.5Probability density for Gaussian vectors in terms ofprincipal axes228 7.3.6Fourier transforms for joint densities230 7.4Linear functionals and filters for random processes231 7.4.1Gaussian processes defined over orthonormalexpansions232 7.4.2Linear filtering of Gaussian processes233 7.4.3Covariance for linear functionals and filters234 7.5Stationarity and related concepts235 7.5.1Wide-sense stationary (WSS) random processes236 7.5.2Effectively stationary and effectively WSSrandom processes238 7.5.3Linear functionals for effectively WSS random processes239 7.5.4Linear filters for effectively WSS random processes239 7.6Stationarity in the frequency domain242 7.7White Gaussian noise244 7.7.1The sinc expansion as an approximation to WGN246 7.7.2Poisson process noise247 7.8Adding noise to modulated communication248 7.8.1Complex Gaussian random variables and vectors250 7.9Signal-to-noise ratio251 7.10Summary of random processes254 7.11Appendix: Supplementary topics255 7.11.1Properties of covariance matrices255 7.11.2The Fourier series expansion of a truncated random process257 7.11.3Uncorrelated coefficients in a Fourier series259 7.11.4The Karhunen–Loeve expansion262 7.12Exercises263 8Detection, coding, and decoding268 8.1Introduction268 8.2Binary detection271 8.3Binary signals in white Gaussian noise273 8.3.1Detection for PAM antipodal signals273 8.3.2Detection for binary nonantipodal signals275 8.3.3Detection for binary real vectors in WGN276 8.3.4Detection for binary complex vectors in WGN279 8.3.5Detection of binary antipodal waveforms in WGN281 8.4M-ary detection and sequence detection285 8.4.1M-ary detection285 8.4.2Successive transmissions of QAM signals in WGN286 8.4.3Detection with arbitrary modulation schemes289 8.5Orthogonal signal sets and simple channel coding292 8.5.1Simplex signal sets293 8.5.2Biorthogonal signal sets294 8.5.3Error probability for orthogonal signal sets294 8.6Block coding298 8.6.1Binary orthogonal codes and Hadamard matrices298 8.6.2Reed–Muller codes300 8.7Noisy-channel coding theorem302 8.7.1Discrete memoryless channels303 8.7.2Capacity304 8.7.3Converse to the noisy-channel coding theorem306 8.7.4Noisy-channel coding theorem, forward part307 8.7.5The noisy-channel coding theorem for WGN311 8.8Convolutional codes312 8.8.1Decoding of convolutional codes314 8.8.2The Viterbi algorithm315 8.9Summary of detection, coding, and decoding317 8.10Appendix: Neyman–Pearson threshold tests317 8.11Exercises322 9Wireless digital communication330 9.1Introduction330 9.2Physical modeling for wireless channels334 9.2.1Free-space, fixed transmitting and receiving antennas334 9.2.2Free-space, moving antenna337 9.2.3Moving antenna, reflecting wall337 9.2.4Reflection from a ground plane340 9.2.5Shadowing340 9.2.6Moving antenna, multiple reflectors341 9.3Input/output models of wireless channels341 9.3.1The system function and impulse response for LTV systems343 9.3.2Doppler spread and coherence time345 9.3.3Delay spread and coherence frequency348 9.4Baseband system functions and impulse responses350 9.4.1A discrete-time baseband model353 9.5Statistical channel models355 9.5.1Passband and baseband noise358 9.6Data detection359 9.6.1Binary detection in flat Rayleigh fading360 9.6.2Noncoherent detection with known channel magnitude363 9.6.3Noncoherent detection in flat Rician fading365 9.7Channel measurement367 9.7.1The use of probing signals to estimate the channel368 9.7.2Rake receivers373 9.8Diversity376 9.9CDMA: the IS95 standard379 9.9.1Voice compression380 9.9.2Channel coding and decoding381 9.9.3Viterbi decoding for fading channels382 9.9.4Modulation and demodulation383 9.9.5Multiaccess interference in IS95386 9.10Summary of wireless communication388 9.11Appendix: Error probability for noncoherent detection390 9.12Exercises391 References398 Index400
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