數(shù)字通信原理

前言

　　Digital communication is an enormous and rapidly growing industry， roughly comparable in size to the computer industry. The objective of this text is to study those aspects of digital communication systems that are unique. That is， rather than focusing on hardware and software for these systems (which is much like that in many other fields)， we focus on the fundamental system aspects of modem digital communication.　　Digital communication is a field in which theoretical ideas have had an unusuallypowerful impact on system design and practice. The basis of the theory was developedin 1948 by Claude Shannon， and is called information theory. For the first 25 years or so of its existence， information theory served as a rich source of academic research prob-lems and as a tantalizing suggestion that communication systems could be made more efficient and more reliable by using these approaches. Other than small experiments and a few highly specialized military systems， the theory had little interaction with practice. By the mid 1970s， however， mainstream systems using information-theoretic ideas began to be widely implemented. The first reason for this was the increasing number of engineers who understood both information theory andcommunication system practice. The second reason was that the low cost and increasing processing power of digital hardware made it possible to implement the sophisticated algorithms suggested by information theory. The third reason was that the increasing complexity of communication systems required the architectural principles of information theory.　　The theoretical principles here fall roughly into two categories - the first provides analytical tools for determining the performance of particular systems， and the second puts fundamental limits on the performance of any system. Much of the first category can be understood by engineering undergraduates， while the second category is dis-tinctly graduate in nature. It is not that graduate students know so much more than undergraduates， but rather that undergraduate engineering students are trained to mas-ter enormous amounts of detail and the equations that deal with that detail. They are not used to the patience and deep thinking required to understand abstract performance limits. This patience comes later with thesis research.　　My original purpose was to write an undergraduate text on digital communication，but experience teaching this material over a number of years convinced me that I could not write an honest exposition of principles， including both what is possible and what is not possible， without losing most undergraduates. There are many excellent undergraduate texts on digital communication describing a wide variety of systems，and I did not see the need for another. Thus this text is now aimed at graduate students，but is accessible to patient undergraduates.　　The relationship between theory， problem sets， and engineering/design in an aca-demic subject is rather complex. The theory deals with relationships and analysis for models of real systems. A good theory (and information theory is one of the best)allows for simple analysis of simplified models. It also provides structural principles that allow insights from these simple models to be applied to more complex and realistic models. Problem sets provide students with an opportunity to analyze these highly simplified models， and， with patience， to start to understand the general princi-ples. Engineering deals with making the approximations and judgment calls to create simple models that focus on the critical elements of a situation， and from there to design workable systems.　　The important point here is that engineering (at this level) cannot really be separated from theory. Engineering is necessary to choose appropriate theoretical models， and theory is necessary to find the general properties of those models. To oversimplify，engineering determines what the reality is and theory determines the consequences and structure of that reality. At a deeper level， however， the engineering perception of real-ity heavily depends on the perceived structure (all of us carry oversimplified models around in our heads). Similarly， the structures created by theory depend on engi-neering common sense to focus on important issues. Engineering sometimes becomes overly concerned with detail， and theory becomes overly concerned with mathematical niceties， but we shall try to avoid both these excesses here.　　Each topic in the text is introduced with highly oversimplified toy models. The results about these toy models are then related to actual communication systems， and these are used to generalize the models. We then iterate back and forth between analysis of models and creation of models. Understanding the performance limits on classes of models is essential in this process.

內(nèi)容概要

　　本書是世界通信權(quán)威、信息領(lǐng)域泰斗Robert G. Gallager博士新作，在數(shù)字通信原理的基礎(chǔ)上精煉，重點(diǎn)闡述了理論、問題和工程設(shè)計(jì)之間的關(guān)系。內(nèi)容涉及離散源編碼、量化、信道波形、向量空間和信號(hào)空間、隨機(jī)過程和噪聲、編碼、解碼等數(shù)字通信基本問題，最后還簡(jiǎn)單介紹了無線數(shù)字通信?！　”緯峭ㄐ艑I(yè)高年級(jí)本科生和研究生教材，也可供工程技術(shù)人員參考。

作者簡(jiǎn)介

　　Robert G. Gallager，博士，信息理論界世界級(jí)權(quán)威，美國工程院院士，美國科學(xué)院院士，先后榮獲1990年IEEE榮譽(yù)獎(jiǎng)?wù)隆?003年馬可尼獎(jiǎng)、2004年Dijkstra獎(jiǎng)等多項(xiàng)殊榮。師從信息論創(chuàng)始人香農(nóng)，不但自己科研成果卓著，還為通信領(lǐng)域培育了很多優(yōu)秀人才，包括《無線通信基礎(chǔ)》的作者David Tse。

書籍目錄

1　Introduction to digital communication　　1.1　Standardized interfaces and layering　　1.2　Communication sources　　　1.2.1　Source coding　　1.3　Communication channels　　　1.3.1　Channel encoding (modulation)　　　1.3.2　Error correction　　1.4　Digital interface　　　1.4.1　Network aspects of the digital interface　　1.5　Supplementary reading　2　Coding for discrete sources　　2.1　Introduction　　2.2　Fixed-length codes for discrete sources　　2.3　Variable-length codes for discrete sources　　　2.3.1　Unique decodability　　　2.3.2　Prefix-free codes for discrete sources　　　2.3.3　The Kraft inequality for prefix-free codes　　2.4　Probability models for discrete sources　　　2.4.1　Discrete memoryless sources　　2.5　Minimum L for prefix-free codes　　　2.5.1　Lagrange multiplier solution for the minimum L　　　2.5.2　Entropy bounds on L　　　2.5.3　Huffman’s algorithm for optimal source codes　　2.6　Entropy and fixed-to-variable-length codes　　　2.6.1　Fixed-to-variable-length codes　　2.7　The AEP and the source coding theorems　　　2.7.1　The weak law of large numbers　　　2.7.2　The asymptotic equipartition property　　　2.7.3　Source coding theorems　　　2.7.4　The entropy bound for general classes of codes　　2.8　Markov sources　　　2.8.1　Coding for Markov sources　　　2.8.2　Conditional entropy　　2.9　Lempel-Ziv universal data compression　　　2.9.1　The LZ77 algorithm　　　2.9.2　Why LZ77 works　　　2.9.3　Discussion　　2.10　Summary of discrete source coding　　2.11　Exercises　3　Quantization　　3.1　Introduction to quantization　　3.2　Scalar quantization　　　3.2.1　Choice of intervals for given representation points　　　3.2.2　Choice of representation points for given intervals　　　3.2.3　The Lloyd-Max algorithm　　3.3　Vector quantization　　3.4　Entropy-coded quantization　　3.5　High-rate entropy-coded quantization　　3.6　Differential entropy　　3.7　Performance of uniform high-rate scalar quantizers　　3.8　High-rate two-dimensional quantizers　　3.9　Summary of quantization　　3.10　Appendixes　　　3.10.1　Nonuniform scalar quantizers　　　3.10.2　Nonuniform 2D quantizers　　3.11　Exercises　4　Source and channel waveforms　　4.1　Introduction　　　4.1.1　Analog sources　　　4.1.2　Communication channels　　4.2　Fourier series　　　4.2.1　Finite-energy waveforms　　4.3　L2 functions and Lebesgue integration over[-T/2,T/2]　　　4.3.1　Lebesgue measure for a union of intervals　　　4.3.2　Measure for more general sets　　　4.3.3　Measurable functions and integration over [-T/2,T/2]　　　4.3.4　Measurability of functions defined by other functions　　　4.3.5　L1 and L2 functions over [-T/2,T/2]　　4.4　Fourier series for L2 waveforms　　　4.4.1　The T-spaced truncated sinusoid expansion　　4.5　Fourier transforms and L2 waveforms　　　4.5.1　Measure and integration over R　　　4.5.2　Fourier transforms of L2 functions　　4.6　The DTFT and the sampling theorem　　　4.6.1　The discrete-time Fourier transform　　　4.6.2　The sampling theorem　　　4.6.3　Source coding using sampled waveforms　　　4.6.4　The sampling theorem for[Δ-W,Δ+W]　　4.7　Aliasing and the sinc-weighted sinusoid expansion　　　4.7.1　The T-spaced sinc-weighted sinusoid expansion　　　4.7.2　Degrees of freedom　　　4.7.3　Aliasing — a time-domain approach　　　4.7.4　Aliasing — a frequency-domain approach　　4.8　Summary　　4.9　Appendix: Supplementary material and proofs　　　4.9.1　Countable sets　　　4.9.2　Finite unions of intervals over [-T/2,T/2]　　　4.9.3　Countable unions and outer measure over [-T/2,T/2]　　　4.9.4　Arbitrary measurable sets over[-T/2,T/2]　　4.10　Exercises　5　Vector spaces and signal space　6　Channels, modulation, and demodulation　7　Random processes and noise　8　Detection, coding, and decoding　9　Wireless digital communication　References　Index　

章節(jié)摘錄

　　For any encoder, there must be a decoder that maps the encoded bit sequenceback into the source symbol sequence. For prefix-free codes on k-tuples of sourcesymbols, the decoder waits for each variable-length codeword to arrive, maps it intothe corresponding k-tuple of source symbols, and then starts decoding for the nextk-tuple. For fixed-to-fixed-length schemes, the decoder waits for a block of codesymbols and then decodes the corresponding block of source symbols.　In general, the source produces a nonending sequence X1, X2　of source letterswhich are encoded into a nonending sequence of encoded binary digits. The decoderobserves this encoded sequence and decodes source symbol Xn when enough bits havearrived to make a decision on it.　For any given coding and decoding scheme for a given iid source, define the rvDn as the number of received bits that permit a decision on Xn = X1……Xn. Thisincludes the possibility of coders and decoders for which some sample source stringsxn are decoded incorrectly or postponed infinitely. For these xn, the sample value ofDn is taken to be infinite. It is assumed that all decisions are final in the sense thatthe decoder cannot decide on a particular xn after observing an initial string of theencoded sequence and then change that decision after observing more of the encodedsequence. What we would like is a scheme in which decoding is correct with highprobability and the sample value of the rate, Dn/n, is small with high probability.What the following theorem shows is that for large n, the sample rate can be strictlybelow the entropy only with vanishingly small probability. This then shows that theentropy lowerbounds the data rate in this strong sense.

媒體關(guān)注與評(píng)論

　　“Gallager教授是一位傳奇人物……他的書見解獨(dú)到、敘述方式簡(jiǎn)明扼要，備受學(xué)生推崇?！薄　　剩的螤柎髮W(xué)教授　　“本書必將成為業(yè)界經(jīng)典參考！”　　——今井秀樹，東京大學(xué)教授　　“這是一本詳盡的數(shù)字通信教材，值得每一位通信專業(yè)師生和相關(guān)工程人員擁有?！薄　　猅elatar，洛桑聯(lián)邦理工學(xué)院教授

編輯推薦

　　國際信息領(lǐng)域泰斗Robert G. Gallager博士最新力作。　　香農(nóng)的信息論中有一個(gè)重要結(jié)論，即信源/信道分離定理：如果能夠以任意某種方式通過信道傳輸信源，那么也一定能夠通過二進(jìn)制接口傳輸該信源。這就為數(shù)字通信成為通信系統(tǒng)的標(biāo)準(zhǔn)形式提供了理論依據(jù)。另外，數(shù)字電路成本低廉、性能可靠、易于小型化，更容易實(shí)現(xiàn)。因此，近十年來，數(shù)字通信技術(shù)發(fā)展迅猛，已經(jīng)深入人們?nèi)粘Ｉ畹拿總€(gè)角落，這也驅(qū)使大量人才投身數(shù)字通信領(lǐng)域?！　　稊?shù)字通信原理(英文版)》融會(huì)了Gallager博士數(shù)十年的教學(xué)科研心得，介紹了信息論方面的基本概念及其對(duì)通信系統(tǒng)設(shè)計(jì)的作用，旨在幫助數(shù)字通信領(lǐng)域的師生和工程技術(shù)人員理解數(shù)字通信背后的基本原理?！稊?shù)字通信原理(英文版)》內(nèi)容全面，包括了數(shù)字通信基本知識(shí)，離散信源的編碼，量化，信源波形與信道波形，向量空間與信號(hào)空間，信道、調(diào)制與解調(diào)，隨機(jī)過程與噪聲，信號(hào)的檢測(cè)與編解碼，無線通信，等等。書中為數(shù)字通信原理搭建了一個(gè)簡(jiǎn)單的框架，以直觀、簡(jiǎn)潔的方式介紹了復(fù)雜的現(xiàn)代通信系統(tǒng)?！　　稊?shù)字通信原理(英文版)》甫一出版，即被業(yè)界奉為經(jīng)典，目前已被麻省理工學(xué)院等世界級(jí)名校作為教材。配有習(xí)題答案，讀者可登錄圖靈公司網(wǎng)站免費(fèi)注冊(cè)獲取。

圖書封面

圖書標(biāo)簽Tags

無

評(píng)論、評(píng)分、閱讀與下載

---

    數(shù)字通信原理 PDF格式下載

---