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Unformatted text preview: Hang: Info Theory (Intr) 2007.2 Hang: Info Theory (Intr) 2007.2 Introduction
1. Shannon's Information Theory 2. Source Coding Theorem 3. Channel Coding Theorem 4. Rate Distortion Theorem Appendix A: Historical Notes  Shannon Signals
Time Discretetime Continuoustime Discreteamplitude Digital Discretetime Analog Continuousamplitude (Sampleddata) (Waveform) Amplitude Block Diagram of a Communication System
Information source Source encoder Channel encoder Modulation (Writing unit) Noise / Interference Transmission channel (Storage medium) Destination Source decoder Channel decoder Demodulator (Read unit) a. Source coding: reduce data redundancy b. Channel coding: reduce (channel) noise effect c. Modulation: select waveforms to cope with (analog) channel constraints (and noise) Coding Mapping
1 2 Hang: Info Theory (Intr) 2007.2 Hang: Info Theory (Intr) 2007.2 1. Source coding: Convert the original symbol stream to minimum binary representation 2. Channel Coding: Convert the original symbol stream to (noise) robust symbol steam 3. Modulation: Map the original symbol steam to waveforms of adequate form (meet channel constraints) Basic Questions in Information and Communication Theory (Blahut, pp.2~3)
Information theory attempts to answer a number of very basic questions: 1. What is Information? That is, how do we measure it? 2. What are the fundamental limits on the transmission of information? 3. What are the fundamental limits on the extraction of Information from the environment? 4. What are the fundamental limits on the compression and refinement of information? 5. How should devices be designed to approach these limits?
3 6. How closely do existing devices approach these limits? Information theory includes the quest for a theory of optimum communication waveforms. When restricted to this quest, the subject matter of information theory can be viewed as the existing answers to a sequence of successively weaker queries: 1. What is the best waveform family for transmitting k bits in T seconds through a specified channel? (For example, how should we send bits through an additive gaussiannoise channel with an average power constraint and a bandwidth constraint on the transmitter?) 2. What is the performance of the best such waveform family for a continuoustime channel (even though the waveform itself may be unknown)? 3. What is the approximate performance of the best waveform family (or code) for transmitting information through a discrete memoryless channel such as a binary channel? C.E. Shannon publishes a series of papers that attempt to answer the above questions. His papers 4 Hang: Info Theory (Intr) 2007.2 Hang: Info Theory (Intr) 2007.2 stimulated a new research field, the socalled Shannon Theory or Information Theory. Essentially his theory has three main theorems: Source coding theorem, channel coding theorem and rate distortion theorem. 1. Source Coding Theorem (Shannon's first theorem)
A simplified version of this theorem can be stated as follows: Given a discrete memoryless source (DMS) S of entropy H (S) , it is possible to encode it with an instantaneous code having the average codeword length L and L H ( S) + , for any > 0 . Conversely, it is not possible to encode such a source with an arbitrary small error with an average length shorter than H (S) .
(Abramson, p.73; Blahut, p.75; Lafrance, p. 199; C&T, p. 88) The entropy is a measure of the average information content per source symbol. (Least number of bits to represent, on the average, an information source.) The source coding theorem is also known as the "noiseless coding theorem" in the sense that it establishes the condition for errorfree encoding to be possible. 5 6 Hang: Info Theory (Intr) 2007.2 Hang: Info Theory (Intr) 2007.2 2. Channel Coding Theorem (Shannon's 2nd theorem)
A simplified version of this theorem is as follows: Given a channel with capacity C, for R < C and any > 0 , there exists a block code C of length n and rate R whose block decoding error PE < . Conversely, if R > C , then PE cannot be arbitrarily small.
(Abramson, p.166; Blahut, p.174,163; Lafrance, p. 208; C&T, p. 198; R&C, p.16, last paragraph) 3. Rate Distortion Theorem (Shannon's 3rd theorem)
What is the minimum bit rate representing a source when a certain amount of distortion is allowed? This may be viewed as an extension of the noiseless source coding theorem. A simplified version of the socalled rate distortion theorem is as follows. A rate distortion code is a mapping from the original message to its approximation. Because the reconstruction is not perfect, a distortion is thus introduced.
x Ratedistortion Code y The theorem specifies the channel capacity C as a fundamental limit on the rate at which the transmission of reliable errorfree message can take place over a welldefined channel, say, DMC.
x Channel
alphabet X
alphabet Y alphabet X alphabet Y y Theorem: Given a DMS X and single letter distortion measure d ( x, y ) , and let R(D ) be the rate distortion function of X with respect to d (,) . Then, for any > 0 and D0 , there exist a code of block length n with the coding rate bounded by R < R( D0 ) + and with the dis8 The Channel Coding Theorem is also known as "noisy coding theorem". 7 Hang: Info Theory (Intr) 2007.2 Hang: Info Theory (Intr) 2007.2 tortion bounded by D < D0 + . Conversely, if the rate is less than R( D0 ) , then the distortion must exceed D0 .
(Blahut, p.225; Lafrance, p. 385; C&T, p. 342) Appendix A. Historical Notes: Shannon
Claude Elwood Shannon (19162001)
The seeds of Shannon's information theory appeared first in a Bell Labs classified memorandum dated 1 September 1945, "A Mathematical Theory of Cryptography" (revised form was published in BSTJ, 1949.) Shannon's work on cryptography problem at Bell Labs brought him into contact with two scholars, R.V.L. Hartley and H. Nyquist, who were major consultants to the cryptography research. Nyquist (1924) has shown that a certain bandwidth was necessary in order to send telegraph signals at a definite rate. Hartley (1928) had attempted to quantify of information as the logarithm of the number of possible message built from a pool of symbols. Shannon was aware of Norbert Wiener's work on cybernetics (He cited Wiener's book in his two 1948 articles on information theory). Wiener had recognized R(D) is characterized by the jointprob of X and Y (and d(x,y)). In transmitting continuousamplitude signals using digital (binary) representations, it is unavoidable to allow distortion. The entropy (distortionless representation) of a real (floating) number may be infinity, but its ratedistortion function (with a reasonable small amount of distortion) is welldefined (finite value). 9 10 Hang: Info Theory (Intr) 2007.2 Hang: Info Theory (Intr) 2007.2 that the communication of information was a problem in statistics. Shannon took a mathematics course from Norbert Wiener when he was studying at MIT, and Shannon had accessed to the "Yellow Peril" report (Wiener's book before publication) while he worked on cryptographic research. Major Publications of Shannon A Mathematical Theory of Communication, I, II, published in Bell Syst. Tech. J, 1948, pp.379423, 623656. Communication Theory of Secrecy Systems, published in BSTJ, 1949, pp.656715. Coding Theorems for a Discrete Source with a Fidelity Criterion, IRE Nat. Cov. Rec. Part 4, 1959, pp.142163. Paperback edition of "The Mathematics Theory of Communication" was published by University of Illinois Press, 1963. 32,000 copies have been sold up to 1964. In a review of the accomplishments of Bell Labs. in communication science, Millman in his edited book (1984), "A history of engineering and science in the Bell system: Communication Science (19251980)" says: "Probably the most spectacular development in communication mathematics to take place at Bell Laboratories was the formulation in the 1940's of in11 formation theory by C. E. Shannon." In his 1948 articles on information theory, Shannon credits J.W. Tukey (a professor at Princeton Univ.) with suggesting the word "bit" as a measure of information. In 1984, J.L. Massey wrote a paper "Information Theory: the Copernican System of Communications." Published in IEEE Communications Magazine, vol. 22, No. 12, pp. 2628. The Shannon's theory of information is the scientific basis of communications in the same sense that the Copernicus' heliocentric theory is the scientific basis of astronomy. Commemorate Issue, "Fifty Years of Shannon Theory," IEEE Transaction on Information Theory, Oct. 1998. Vol.44, No.6, (19481998) 12 Hang: Info Theory (Intr) 2007.2 References 1. N. Abramson, Information Theory and Coding, McGrawHill, 1963. 2. P. Lafrance, Fundamental Concepts in Communications, PrenticeHall, 1990. 3. R.E. Blahut, Principles and Practice of Information Theory, AddisonWesley, 1987, 1990 4. T.M. Cover and J.A. Thomas, Elements of Information Theory, John Wiley and Sons, 1991. 5. S. Verd, "Fifty Years of Shannon Theory," IEEE
Transaction on Information Theory, pp.20572078, Oct. 1998. 13 ...
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This note was uploaded on 07/21/2009 for the course EE IEE6533 taught by Professor H.m.hang during the Fall '07 term at National Chiao Tung University.
 Fall '07
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