信息论之父Shannon的经典之作《通信的数学理论》(1948).pdf

A Mathematical Theory of Communication By C. E. SHANNON INTRODUCTION THE recent development of various methods of modulation such as PCM and PPM which exchange bandwidth for signaltonoise ratio has intensified the interest in a general theory of communication. A basis for such a theory is containe
physical counterparts. We may roughly classify communication systems into three main categories: discrete continuous and mixed. By a discrete system we will mean one in which both the message and the signal are a sequence of discrete symbols. a typical case is telegraphy where the message is a sequence of letters and the signal a sequence of dots dashes and spaces a continuous system is one in which the message and signal are both treated as continuous functions, e. g, radio or television. A mixed system is one in which both discrete and continuous variables appear, c g PCM transmission of specch We first consider the discrete case. This case has applications not only in communication theory, but also in the theory of computing machines, the design of telephone exchanges and other fields. In addition the discrete case forms a foundation for the continuous and mixed cases which will be treated in the second half of the paper PART I: DISCRETE NOISELESS SYSTEMS 1. THE DISCRETE NOISELESS CHANNEL Teletype and telegraphy are two simple examples of a discrete channel for transmitting information. Gen erally, a discrete channel will mean a system whereby a sequence of choices from a finite set of elementary symbols SI,..., Sn can be transmitted from one point to another. Each of the symbols Si is assumed to have a certain duration in time t; seconds(not necessarily the same for different Si, for example the dots and dashes in telegraphy ) It is not required that all possible sequences of the Si be capable of transmission on the system; certain sequences only may be allowed. These will be possible signals for the channel. Thus in telegraphy suppose the symbols are:(1)a dot, consisting of line closure for a unit of time and then line open for a unit of time;(2)A dash, consisting of three time units of closure and one unit open; (3)A letter space consisting of, say, three units of line open; (4 )A word space of six units of line open. We might place the restriction on allowable sequences that no spaces follow each other (for if two letter spaces are adjacent it is identical with a word space). The question we now consider is how one can measure the capacity of such a channel to transmit information In the teletype case where all symbols are of the same duration, and any sequence of the 32 symbols is allowed the answer is easy. Each symbol represents five bits of information. If the system transmits n symbols per second it is natural to say that the channel has a capacity of 5n bits per second. This does not mean that the teletype channel will always be transmitting information at this ratethis is the maximum possible rate and whether or not the actual rate reaches this maximum depends on the source of information which feeds the channel, as will appear later In the more general case with different lengths of symbols and constraints on the allowed sequences, we make the following definition Definition: The capacity C of a discrete channel is given by log n(t T where N(T) is the number of allowed signals of duration T It is easily seen that in the teletype case this reduces to the previous result. It can be shown that the limit in question will exist as a finite number in most cases of interest. Suppose all sequences of the symbols S1,..., Sn are allowed and these symbols have durations tI, ... tn. What is the channel capacity? If N(t) represents the number of sequences of duration t we have N(t)=N(tt1)+M(tt2)+…+N(ttn) The total number is equal to the sum of the numbers of sequences ending in S1, S2,..., Sn and these are N(t11), N(tt2),., N(tUn), respectively. According to a wellknown result in finite differences, N(t) is then asymptotic for large t to Xo where Xo is the largest real solution of the characteristic equation X4+X2+…+X=1 and therefore g In case there are restrictions on allowed sequences we may still often obtain a difference equation of this type and find C from the characteristic equation. In the telegraphy case mentioned above N(1)=N(t2)+N(4)+N(t5)+N(t7)+N(t8)+N(t10) as we see by counting sequences of symbols according to the last or next to the last symbol occurring Hence C islogHo where uo is the positive root of 1=u2+44+u5+u'+u+ul0. Solving this we find C=0.539 very general type of restriction which may be placed on allowed sequences is the following: We imagine a number of possible states a1, a2, .. am. For cach state only certain symbols from the sct SI: ., S, can be transmitted (different subsets for the different states). When one of these has been transmitted the state changes to a new state depending both on the old state and the particular symbol transmitted. The telegraph case is a simple example of this. There are two states depending on whether or not a space was the last symbol transmitted. If so, then only a dot or a dash can be sent next and the state always changes If not, any symbol can be transmitted and the state changes if a space is sent, otherwise it remains the same The conditions can be indicated in a linear graph as shown in Fig. 2. The junction points correspond to the DOT DOT LETTER SPACE DASH WORD SPACE Fig. 2Graphical representation of the constraints on telegraph symbols states and the lines indicate the symbols possible in a state and the resulting state. In Appendix I it is shown that if the conditions on allowed sequences can be described in this form C will exist and can be calculated in accordance with the following result Theorem 1: Let bi be the duration of the sth symbol which is allowable in state i and leads to state Then the channel capacity C is equal to log w where w is the largest real root of the determinant equation 3) 6:=0 Where el ifi=j and is zero otherwise For example, in the telegraph case(Fig. 2)the determinant is (W2+W (W3+W5)(W2+Wy1 On expansion this leads to the equation given above for this case 2. THE DISCRETE SOURCE OF INFORMATION We have seen that under very general conditions the logarithm of the number of possible signals in a discrete channel increases linearly with time. The capacity to transmit information can be specified by giving this rate of increase, the number of bits per second required to specify the particular signal used We now consider the information source. How is an information source to be described mathematically and how much information in bits per second is produced in a given source? The main point at issue is the effect of statistical knowledge about the source in reducing the required capacity of the channel, by the use of proper encoding of the information. In telegraphy, for example, the messages to be transmitted consist of sequences of letters. These sequences, however, are not completely random. In general, they form sentences and have the statistical structure of, say, English. The letter E occurs more frequently than Q, the sequence TH more frequently than XP, etc. the existence of this structure allows one to make a saving in time (or channel capacity by properly encoding the message sequences into signal sequences. This is already done to a limited extent in telegraphy by using the shortest channel symbol, a dot for the most common English letter E; while the infrequent letters, Q, X, Z are represented by longer sequences of dots and dashes. This idea is carried still further in certain commercial codes where common words and phrases are represented by four or fiveletter code groups with a considerable saving in average time. The standardized greeting and anniversary telegrams now in use extend this to the point of encoding a sentence or two into a relatively short sequence of numbers We can think of a discrete source as generating the message, symbol by symbol. It will choose succes sive symbols according to certain probabilities depending, in general, on preceding choices as well as the particular symbols in question. A physical system, or a mathematical model of a system which produces such a sequence of symbols governed by a set of probabilities, is known as a stochastic process 3 We may consider a discrete source, therefore, to be represented by a stochastic process. Conversely, any stochastic process which produces a discrete sequence of symbols chosen from a finite set may be considered a discrete source. This will include such cases as 1. Natural written languages such as English, German, Chinese example, the quantized speech from a PCM transmitter, or a quantized television signalOceSs. For 2. Continuous information sources that have been rendered discrete by some quantizing pr 3. Mathematical cases where we merely define abstractly a stochastic process which generates a se quence of symbols. The following are examples of this last type of sourc (A) Suppose we have five letters A, B, C, D,e which are chosen each with probability. 2, successive choices being independent. This would lead to a sequence of which the following is a typical le BDCBCECCCADCBDDAAECEEA ABBDAEECACEEBAEECBCEAD This was constructed with the use of a table of random numbers .4 (B)Using the same five letters let the probabilities be, 4, 1, 2, 2,. 1, respectively, with successive choices independent. a typical message from this source is then AAACDCBDCEAADADACEDA EADCABEDADDCECAAAAAD (C)A more complicated structure is obtained if successive symbols are not chosen independently but their probabilities depend on preceding letters. In the simplest case of this type a choice depends only on the preceding letter and not on ones before that. The statistical structure can then be described by a set of transition probabilities pi(), the probability that letter i is followed by letter. The indices i and j range over all the possible symbols. a second equivalent way of specifying the structure is to give the "digram"probabilities p(i,j), i.e., the relative frequency of the digram ij. The letter frequencies p(i), (the probability of letter i), the transition probabilities See, for example, S Chandrasekhar, "Stochastic Problems in Physics and Astronomy, "Reviews of Modern Physics, V. 15, No I anuary 1943, p. 1. #Kendall and Smith, Tables of Random Sumpling Numbers, Cambridge, 1939 P: and the digram probabilities p(i j) are related by the following formulas P()=·p(,)=·p(,1)=·P()P/() p(i,)=p()p;( ()=●p(i1j)= As a specific example suppose there are three letters A, B, C with the probability tables pil AB c A B C AO 9 a O 1515 i B 0 B i B 0 C A typical message from this source is the following ABBABABABABABABBBABBBBBABABABABABBBACACAB BABBBBABBABACBBBABA The next increase in complexity would involve trigram frequencies but no more. The choice of a letter would depend on the preceding two letters but not on the message before that point.a set of trigram frequencies p(i,j, k) or equivalently a set of transition probabilities pii (k)would be required. Continuing in this way one obtains successively more complicated stochastic pro cesses. In the general ngram case a set of ngram probabilities p(il, i2, . .. in)or of transition probabilities Pili,i,(in) is required to specify the statistical structure (D) Stochastic processes can also be defined which produce a text consisting of a sequence of Suppose there are five letters A, B, C, D,E and 16 words in the language with associated probabilities 10A 16 BEBE 11 CABED 04 DEB 04 ADEB 04 BED 05 CEED IS DEED 05 adee 02 beed o 8 DAB Ol EAB O1 BADD , 05 CA 04 DAD 05 EE Suppose successive"words "are chosen independently and are separated by a space. a typical message might be DAB EEA BEBE DEED DEB ADEEADEE EE DEB BEBEBEBE BEBE ADEE BED DEED DEED CEED ADEE A DEED DEED BEBE CABED BEBE BED DAB DEED ADEB If all the words are of finite length this process is equivalent to one of the preceding type but the description may be simpler in terms of the word structure and probabilities. We may also generalize here and introducc transition probabilities between words, etc These artificial languages are useful in constructing simple problems and examples to illustrate vari ous possibilities. We can also approximate to a natural language by means of a series of simple artificial languages. The zeroorder approximation is obtained by choosing all letters with the same probability and independently. The firstorder approximation is obtained by choosing successive letters independently but cach letter having the same probability that it has in the natural language. Thus, in the firstorder ap proximation to English, E is chosen with probability. 12(its frequency in normal English) and W with probability. 02, but there is no influence between adjacent letters and no tendency to form the preferred Leller, digraIn and trigrain frequencies are given in Secred and Urgent by Fletcher Prall, Blue Ribbon Books, 1939. Word frequen cics arc tabulated in Relative Frequency of English Speech Sounds, G. Dcwcy, Harvard Univcrsity Press, 1923 digrams such as TH, ED, etc. In the secondorder approximation, digram structure is introduced. After a letter is chosen, the next one is chosen in accordance with the frequencies with which the various letters follow the first one. This requires a table of digram frequencies pi(i). In the thirdorder approximation trigram structure is introduced. Each letter is chosen with probabilities which depend on the preceding two letters 3. THE SERIES OF APPROXIMATIONS TO ENGLISH To give a visual idea of how this series of processes approaches a language typical sequences in the approx imations to English have been constructed and are given below. In all cases we have assumed a 27symbo alphabet, the 26 letters and a space Zeroorder approximation(symbols independent and equiprobable) XFOML RXKHRJFFJUJ ZLPWCFWKCYJ FFJEYVKCQSGHYD QPAAMKBZAACIBZL HJQD 2. Firstorder approximation(symbols independent but with frequencies of English text) Ocro Hli Rgwr NmielwiS Eu Ll NbnesebYa Th Eei AlhenhTtpa Oobttva NAH BRL 3. Secondorder approximation(digram structure as in English) ON IE ANTSOUTINYS ARE T INCTORE ST BE S DEAMY ACHIN D ILONASIVE TU COOWE AT TEASONARE FUSO TIZIN ANDY TOBE SEACE CTISBE 4. Thirdorder approximation(trigram structure as in english IN NO IST LAT WHEY CRATICT FROURE BIRS GROCID PONDENOME OF DEMONS TURES OF THE REPTAGIN IS REGOACTIONA OF CRE 5. Firstorder word approximation. Rather than continue with tetragram,., ngram structure it is easier and better to jump at this point to word units. Here words are chosen independently but with their appropriate frequencies REPRESENTING AND SPEEDILY IS AN GOOD APT OR COME CAN DIFFERENT NAT URAL HERE HE THE A IN CAME THE TO OF TO EXPERT GRAY COME TO FURNISHES THE LINE MESSAGE HAD BE THESE 6. Secondorder word approximation. The word transition probabilities are correct but no further struc ture is included THE HEAD AND IN FRONTAL ATTACK ON AN ENGLISH WRITER THAT THE CHAR ACTER OF THIS POINT IS THEREFORE ANOTHER METHOD FOR THE LETTERS THAT THE TIME OF WHIO EVER TOLD THE PROBLEM FOR AN UNEXPECTED The resemblance to ordinary English text increases quite noticeably at each of the above steps. Note that these samples have reasonably good structure out to about twice the range that is taken into account in their construction. Thus in (3)the statistical process insures reasonable text for twoletter sequences, but four letter sequences from the sample can usually be fitted into good sentences. In(6) sequences of four or more words can easily be placed in sentences without unusual or strained constructions. The particular sequence of ten words attack on an English writer that the character of this"is not at all unreasonable. It appears then that a sufficiently complex stochastic process will give a satisfactory representation of a discrete source The first two samples were constructed by the use of a book of random numbers in conjunction with (for example 2)a table of letter frequencies. This method might have been continued for(3), (4)and(5), since digram, trigram and word frequency tables are available, but a simpler equivalent method was used To construct 3)for example, one opens a book at random and selects a letter at random on the page. This letter is recorded. The book is then opened to another page and one reads until this letter is encountered The succeeding letter is then recorded. Turning to another page this second letter is searched for and th succeeding letter recorded, etc. A similar process was used for (4),(5) and (6). It would be interesting if further approximations could be constructed, but the labor involved becomes enormous at the next stage 4. GRAPHICAL REPRESENTATION OF A MARKOFF PROCESS Stochastic processes of the type described above are known mathematically as discrete markoff processes and have been extensively studied in the literature. The general case can be described as follows: There exist a finite number of possible"states"of a system; S1, S2,..., Sn. In addition there is a set of transition probabilities; pi() the probability that if the system is in state Si it will next go to state Si. To make this Markoff process into an information source we need only assume that a letter is produced for each transition from one state to another. The states will correspond to the "residue of influence? "from preceding letters The situation can be represented graphically as shown in Figs. 3, 4 and 5. The"states "are the junction B Fig 3A graph corresponding to the source in example B points in the graph and the probabilities and letters produced for a transition are given beside the correspond ing line. Figure 3 is for the example B in Section 2, while Fig. 4 corresponds to the example C. In Fig 3 Fig 4A graph corresponding to the source in example c there is only one state since successive letters are independent. In Fig. 4 there are as many states as letters If a trigram example were constructed there would be at most n? states corresponding to the possible of letters preceding the one being chosen figure 5 is a graph for the case of word structure in example d Here S corresponds to the "space" symbol 5. ERGODIC AND MIXED SOURCES As we have indicated above a discrete source for our purposes can be considered to be represented by a Markoff process. Among the possible discrete Markoff processes there is a group with special properties of significance in communication theory. This special class consists of theergodic'" processes and we shall call the corresponding sources ergodic sources. although a rigorous definition of an ergodic process is somewhat involved, the generalidea is simple. In an ergodic process every sequence produced by the process bFor a detailed trealment see M. Frechet, Methode des fonctions arbitraires. Theorie des evenements en chuine dans le cas d'un nombre fini d'etats possibles. Paris, GauthierVillars, 1938 is the same in statistical properties. Thus the letter frequencies, digram frequencies, etc, obtained from particular sequences, will, as the lengths of the sequences increase, approach definite limits independent of the particular sequence. Actually this is not true of every sequence but the set for which it is false has probability zero. Roughly the ergodic property means statistical homogeneity All the examples of artificial languages given above are ergodic. This property is related to the structure of the corresponding graph. If the graph has the following two propertiesthe corresponding process will b ergodIc 1. The graph does not consist of two isolated parts a and B such that it is impossible to go from junction points in part a to junction points in part B along lines of the graph in the direction of arrows and also impossible to go from junctions in part B to junctions in part A 2. A closed series of lines in the graph with all arrows on the lines pointing in the same orientation will be called a"circuit. The"length of a circuit is the number of lines in it Thus in Fig. 5 scries beBES is a circuit of length 5. The second property required is that the greatest common divisor of the lengths of all circuits in the graph be one E Fig 5A graph corresponding to the source in example D) If the first condition is satisfied but the second one violated by having the greatest common divisor equal to d> l, the sequences have a certain type of periodic structure. The various sequences fall into d different classes which are statistically the same apart from a shift of the origin (i.e, which letter in the sequence is called letter 1). By a shift of from 0 up to d  I any sequence can be made statistically equivalent to an other A simple example with d =2 is the following: There are three possible letters a, 6, c. Letter a is followed with either b or c with probabilities3 and] respectively. Either b or c is always followed by letter a. Thus a typical sequence is a bacacacabacaba baca This type of situation is not of much importance for our work If the first condition is violated the graph may be separated into a set of subgraphs each of which satisfies the first condition. We will assume that the second condition is also satisfied for each subgraph We have in this case what may be called a"mixed source made up of a number of pure components. The components correspond to the various subgraphs. IfL1, L2, L3, . are the component sources we may write L=p1L+p2L2+p3L3+… These are restatements in terms of the graph of conditions given in Frechet where Pi is the probability of the component source Li Physically the situation represented is this: There are several different sources L1, L2, L3, . which are each of homogeneous statistical structure(i.e they are ergodic). We do not know a priori which is to b used, but once the sequence starts in a given pure component Li, it continues indefinitely according to the statistical structure of that component As an example one may take two of the processes defined above and assume p = 2 and p2=.8.A sequence irom the mixed source L=.2L1+.8L2 would be obtained by choosing first LI or L2 with probabilities. 2 and 8 and after this choice generating a scqucnce from whichever was chosen Except when the contrary is stated we shall assume a source to be ergodic. This assumption enables one to identify averages along a sequence with averages over the ensemble of possible sequences(the probability of a discrepancy being zero). For example the relative frequency of the letter A in a particular infinit sequence will be, with probability one, equal to its relative frequency in the ensemble of sequences If Pi is the probability of state i and pi () the transition probability to state j, then for the process to be stationary it is clear that the Pi must satisfy equilibrium conditions P=·Pp() In the ergodic case it can be shown that with any starting conditions the probabilities P (N)of being in state j after N symbols, approach the equilibrium values as Ny 6. CHOICE UNCERTAINTY AND ENTROPY We have represented a discrete information source as a Markoff process. Can we define a quantity which will measure, in some sense, how much information is produced "by such a process, or better, at what rate information is produced? o. Suppose we have a set of possible events whose probabilitics of occurrence arc P1, P2y., probabilities are known but that is all we know concerning which event will occur. Can we find a measure of how much"choice?' is involved in the selection of the event or of how uncertain we are of the outcome? If there is such a measure, say H(P1, P2,..., Pn), it is reasonable to require of it the following properties 1. H should be continuous in the Pi 2. If all the p: are equal, Pi=5, then H should be a monotonic increasing function of n. With equally likely events there is more choice, or uncertainty, when there are more possible events 3. If a choice be broken down into two successive choices, the original H should be the weighted sum of the individual values of H. The meaning of this is illustrated in Fig. 6. At the left we have three 1/2 1/2 2/3 1/6 Fig.6— Decompos f a choice from thi ssibiliti possibilitiesp1=3, P2=3, P3=6. On the right we first choose between two possibilities each with probability I, and if the second occurs make another choice with probabilities 3,3. The final results have the same probabilities as before We require, in this special case, that H(,3,)=H(2,2)+H(3,3) The coefficient i is because this second choice only occurs half the time 10
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