Title: Bounds on reliability for binary codes in a gaussian channel
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Title: Bounds on reliability for binary codes in a gaussian channel
Physical Description: vii, 86 leaves. : illus. ; 28 cm.
Language: English
Creator: Wood, James Robert, 1931-
Publication Date: 1964
Copyright Date: 1964
 Subjects
Subject: Forms, Binary   ( lcsh )
Forms (Mathematics)   ( lcsh )
Reliability (Engineering)   ( lcsh )
Electrical Engineering thesis Ph. D
Dissertations, Academic -- Electrical Engineering -- UF
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
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Thesis: Thesis--University of Florida, 1964.
Bibliography: Bibliography: leaves 84-85.
Additional Physical Form: Also available on World Wide Web
General Note: Manuscript copy.
General Note: Vita.
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Bibliographic ID: UF00097952
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
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Resource Identifier: alephbibnum - 000565603
oclc - 13561528
notis - ACZ2021

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BOUNDS ON RELIABILITY FOR BINARY CODES

IN A GAUSSIAN CHANNEL















By
JAMES ROBERT WOOD











A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL OF
THE UNIVERSITY OF FLORIDA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY












UNIVERSITY OF FLORIDA
August, 1964

















ACKNOWLEDGMENTS


The author wishes to express his deep appreciation to

Dr. W. W. Peterson and Dr. T. S. George for their guidance and

counsel during the course of the research reported herein. A

large measure of gratitude is due also to the International

Business Machines Corporation, whose extraordinarily generous

support made this work possible.














TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS . . . . . . . . ii

LIST OF FIGURES . . . . . . v

ABSTRACT . . . . . . ..... ...... .. vi

SECTION

I. INTRODUCTION . . . . . ........ 1

A. The Coding Problem
B. The Investigation
C, The Plan of This Paper

II. THE COMMUNICATION SYSTEM DEFINED . . . . 7

A. The Channel
B. The Code
C. The Noise Distribution
D. The Decoding System
E. Euclidean Distance as Related to
Hamming Distance

III. BOUNDS ON RELIABILITY . . . . . . .. 17

A, The Concept of a Bound
B. First Upper Bound on E(R)
C. First Lower Bound on E(R)
D. Second Upper Bound on E(R)
E. Second Lower Bound on E(R)

IV. COMPARISON WITH THE UNRESTRICTED THEORY . . . 44

A. Preliminary Remarks
B, A Comparison of Upper Bounds
C. A Comparison of Lower Bounds

V. CONCLUSIONS . .. . . . . . . 54

A. Summary of the Investigation
B, Suggestions for Future Work










TABLE OF CONTENTS (Continued)



Page

APPENDIX . . .. . . .. . . . . 71

LIST OF REFERENCES ........... . .. . 84

BIOGRAPHICAL SKETCH . . . . . . . . 86














LIST OF FIGURES I


Figure Page

1. Interpretation of First Lower Bound . . . .. 59

2. Bounds on Reliability--Unrestricted Theory . . .. 60

3. Comparison of Reliability for A = 1/2 . . ... 61

4. Comparison of Reliability for A = 1 . . . ... 62

5. Comparison of Reliability for A = 2 . . . ... 63

6. Comparison of Reliability for A = 3 . . . ... 64

7. Comparison of Reliability for A = 4 . . . ... 65

8. Comparison of Reliability for A = 8 . . . ... 66

9. Comparison of Rate vs. A . . ........ . 67

10. First Lower Bound on Reliability, A = 1/2, 1, 2, 3 .. 68

11. First Lower Bound on Reliability, A = 4 . . . 69

12. First Lower Bound on Reliability, A = 8, 16 ... . 70

13. Low Rate Bound Compared with Sphere-Packing Bound
for p = 0.01 . . ... .. ...... . ...... . 83






T










Abstract of Dissertation Presented to the Graduate Council in
Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy


BOUNDS ON RELIABILITY FOR BINARY CODES IN A GAUSSIAN CHANNEL


By


James Robert Wood


August 8, 1964


Chairman: Dr, T. S. George

Major Department: Electrical Engineering


This paper will report the results of an investigation

of a particular coding method for continuous channels. Specifi-

cally, binary linear codes (group codes) are employed as a coding

method for the inputs to a time-discrete continuous channel.

This channel is presumed to be perturbed only by additive noise

with a Gaussian amplitude distribution which affects each trans-

mitted digit independently.

Shannon has obtained bounds on the optimum probability of

error when the input signals are considered to be sequences of

n real numbers, subject only to the constraint that the signal

power in each sequence be a constant. This is a nominal restric-

tion, resulting in a very general theory. The use of group codes

for the input signal sequences restricts the individual numbers

in the n-number sequence to take on only one of two distinct









values and further requires that the input sequences be capable

of being placed into one-to-one correspondence with a group code.

This is more restrictive than the general case but has the advan-

tage of being a constructive method of establishing the input

sequences.

Four bounds, two upper and two lower, on the reliability

of such a coding method are derived. The upper bounds show that

Lhe reliability of the binary coding is bounded away from the

optimum for some ranges of the transmission rate. The range of

rate over which these bounds give useful information is a func-

tion of the signal power to noise power ratio. The lower bounds

on reliability for the binary coding technique are below those

for the general case for all ranges of transmission rate. This

is an expected result, since the binary case is a special case of

the general theory. For a reasonably broad range of transmission

rate, however, the lower bounds are quite close together, indi-

cating that the binary case can guarantee reliability only

slightly worse than that guaranteed by the general technique.

The results of the investigation show that the use of group

codes as input signal sequences is promising.


vii














SECTION I


INTRODUCTION


A. The Coding Problem


Shannon (1)* has established that it is possible to transmit

information from a source to a receiver over a communications channel

in such a way that the probability that an error will occur can be

made as small as desired, provided that the rate of this information

transmission does not exceed a value called the channel capacity.

This startling and not at all obvious result is achieved by asso-

ciating with the information to be transmitted additional quantities

of data which serve to detect and to correct errors introduced by a

noisy channel. Shannon's classic results, while demonstrating that

such a performance is possible, are in effect existence theorems

which do not provide constructive means for achieving this reliable

information transmission. The coding problem is that of devising

methods of appending redundant data to desired information in order

to achieve these results which are known to be possible.

The purpose of introducing redundancy into messages by

coding is to combat the effects of the noise which is present in






*Underlined numbers in parentheses refer to the List of
References at the end of this paper.

1









the transmission medium or channel. It follows, then, that work in

coding theory has been broadly classified according to the type of

channel over which communication is to take place.

In general, a channel may be considered as a device which

transforms successive input events, each represented by a point x

of an input space X, into output events, each represented by a

point y of an output space Y. This transformation of x to y is

governed by a conditional probability distribution P(Y/X) which is

determined by the noise in the channel.

Channels are usually classified according to the types of

the input and output spaces. If the input and output spaces are

discrete, the channel is said to be discrete. If the input and out-

put spaces are continuous, the channel is said to be continuous.

Discrete-to-continuous and continuous-to-discrete channels would

also be possibilities.

Successive events in a discrete channel form a time-discrete

sequence. However, two possibilities arise in the case of a

continuous space. A point representing an event may be allowed to

change only at specified instants of time. If the channel has input

and output spaces of this type, it is said to be time-discrete with

continuous amplitudes. Alternatively, the point representing an

event may be free to change its value at any time, i.e., to move

continuously. If the channel has input and output spaces of this

type, it is said to be time-continuous with continuous amplitudes.

Coding theory may then be classified according to its application in










discrete channels, time-discrete channels with continuous amplitudes

or time-continuous channels with continuous amplitudes.

Research in coding for the discrete channel has been largely

centered on binary channels, wherein the input event or signal can

have only two states. Results in this area have been voluminous,

both as to coding methods and to evaluation of possible ranges and

bounds on error probabilities for various classes of discrete codes.

Peterson (2) presents a comprehensive study of current practices and

the present state of research in discrete channel coding theory. As

an indication of the complexity of this problem, it is significant

that even today Elias' Error-Free Coding (3) is the only known

example of a constructive error coding technique which permits the

realization of an error probability approaching zero as we increase

without limit the size of the information blocks transmitted, while

maintaining a non-zero information rate. Even this method requires

that information be transmitted at a rate well below channel capacity,

which is the theoretical upper limit,

The search for techniques for incorporation of redundancy

into signals for continuous channels, both time-discrete and time-

continuous, has proceeded largely under the name of signal design,

rather than coding. Much of the error-correcting techniques

considered have been correlation methods. While the object is the

same as coding for discrete channels, the diversity of the mathemati-

cal and conceptual techniques has resulted in relatively little

interplay between coding theory and signal design.








The application of coding theory to continuous channels

has been restricted almost exclusively to time-discrete channels

with continuous amplitudes. Shannon (4) has obtained results

giving the possible limits on error probabilities for a particular

type of input space. Franco and Lachs (5) and Harmuth (6) have

investigated the use of orthogonal functions as signals for a time-

discrete channel in a manner which is reminiscent of discrete

coding theory

There has been to the author's knowledge very little work

done on coding for time-continuous channels with continuous ampli-

tudes except for calculations of channel capacity, typically by

Fano (7). There are two basic reasons for this. First, the transi-

tion from time-discrete to time-continuous channels is mathemati-

cally a formidable step. Second, while many channels which are of

practical interest are of the time-continuous type, they may in

many cases be represented to a satisfactory degree of accuracy as

time-discrete channels, usually through sampling techniques.

B. The Investigation


This paper will report the results of an investigation of a

particular coding method for time-discrete channels with continuous

amplitudes. Specifically, binary linear codes (group codes) will

be employed as a coding method for the inputs to a continuous

channel which is presumed to be perturbed only by additive noise

with a Gaussian amplitude distribution which affects each trans-

mitted digit independently. The signals and the channel will be

more precisely defined in a later section of this paper.










Shannon (4) has obtained results for a channel of this type

when the input signals are considered to be sequences of n real

numbers, subject only to the constraint that the signal power in

each sequence be a constant. This is a nominal constraint, resulting

in a very general theory for this type of channel. The use of group

codes for the input signal sequences will restrict the individual

numbers in the n-number sequences to take on only one of two distinct

values, and will further require that the input sequences be capable

of being placed into one-to-one correspondence with a group code.

This method is much more constrained than the largely unrestricted

signal sequences allowed in the work of Shannon. It has, however,

the advantage of being a constructive method of establishing the input

sequences. The bulk of the investigation is then an establishing of

comparative results between the group code method and the unrestricted

theory.

Specifically, it is desired to compare the reliability of the two

methods. Reliability has a precise definition which will be given

later but it is in essence a measure of the error-correcting capa-

bilities of a code. Exact figures for reliability are generally not

obtainable when a large class of codes is considered due to the many

different codes within a class and also to inherent mathematical dif-

ficulties. Instead, upper and lower bounds on reliability are commonly

obtained. For a specified code length an estimate or bound on reli-

ability does not give a very accurate determination of the probability









of error of the code. However, given a desired level of error proba-

bility, a knowledge of reliability will permit a reasonably sharp

estimate of the required length of the code. This is often the

actual problem faced in coding applications.

Shannon's unrestricted results contain four bounds on reliability,

two upper bounds and two lower bounds. In this investigation, two new

upper and two new lower bounds on reliability are presented. These

bounds give a measure of the loss in reliability incurred when the

restrictive encoding method using group codes is employed. Certain

allied results obtained in the investigation will also be presented.


C. The Plan of This Paper


This paper contains five numbered sections and an appendix.

The first section is this Introduction. Section II defines the

channel and the encoding technique. Section III presents the derivation

of the bounds. Section IV compares the results of this investigation

with the unrestricted case. Section V contains conclusions and

recommendations for further work. Allied results are in the Appendix.













SECTION II


THE COMMUNICATION SYSTEM DEFINED


A. The Channel


The type of communication channel with which this investi-

gation is concerned is termed a "continuous, time-discrete"

channel. Fano (8) describes a continuous, time-discrete channel

as one wherein the input and output events are represented by

points of continuous, Euclidean space, but these points are per-

mitted to change their positions only at specified time instants.

For simplicity, it may be assumed that the input changes once each

second, and that at any given time the input consists of a real

number. The input, then, is a sequence of real numbers which change

once each second. The ith real number will be denoted ui.

The channel is assumed to be perturbed by an additive

noise, whose amplitude has a Gaussian distribution and which affects

each u independently. At the receiver, then, the ith real number

will be observed as ui + ni, where the n are independent Gaussian

random variables, all of which are assumed to have the same

variance N. The assumption that the noise affecting the channel

is of this type results in an admittedly highly idealized channel.

It is felt, however, that understanding this channel thoroughly

will be very helpful if more difficult generalizations are attempted.










In this investigation, the values of the ui are restricted

to be one of two distinct numbers. These numbers could be arbi-

trarily chosen, but it is shown in the Appendix, Section A, that

because of the structure of group codes, minimum signal power will

result when these numbers are chosen as +B, where B is a real

number. Hence, the ith real number observed at the receiver will

have the form +B + n .

This channel may be considered to be a form of sampled

data communication. However, the arguments to be used in developing

the bounds on reliability will be largely geometric, No considera-

tion will be given to the origin of these inputs or to the allied

question of whether a continuous function of time can be adequately

represented in the form given above. These problems arise in the

application of this channel in a sampled system.


B. The Code


The sequences of real numbers used as inputs to the channel

will be arranged as a block code. A block code is a code that uses

sequences of n symbols or n-tuples. Each sequence of n symbols is

termed a code word or code block. With the restriction that each

input symbol may take on only the values +B, there are 2n n-tuples

which could be used. Of this total number, only M of these will

be used as code words. In this paper, it will be further required

that each ensemble of M code words be in one-to-one correspondence

with a binary linear code or group code. Formally, this corre-

spondence can be achieved by mapping a binary linear code into










the set of all n-tuples containing +B as elements, where "1" is

mapped into "B," and "0" is mapped into "-B." The resulting subset

of n-tuples with +B as elements is then in one-to-one correspond-

ence with the binary linear code.

In the notation of group codes, an (n,k) code is a code

of length n in which each code word contains k information digits

and (n-k) redundancy digits. There are M=2k code words in an

(n,k) code,

A convenient interpretation of the ensemble of M code words

is that they represent M points in n-dimensional Euclidean space.

The origin of coordinates in this space is the zero vector, and

a typical point of the ensemble might be B, -B, B, -B,

The points in n- space whose coordinates consist of either B or -B

will be called "binary points." Since coding involves the selec-

tion of M of the possible 2n binary points, each code word will be

a binary point, while the converse is not true. Each binary point

is at the same distance from the origin, since in n-space (9) the

length R of a line is given by


n
R2 = (Xi Y )2
1
i=l

where Xi and Yi are the ith components of the vectors X and Y,

respectively. Consider Y to be the origin and let X be any

signal vector. Then









n
2 2 2
R = B =nB
i=l


Thus, each binary point lies on the surface of an n-dimensional

space of radius BVn.

A motivation for the terminology "unrestricted case" as

applied to the case treated by Shannon (4) is apparent in the fore-

going discussion. When input signal sequences are permitted to

assume any value subject to the constraint of constant power for

each sequence, it can be shown that these sequences may lie any-

where on the surface of an n-dimensional sphere. When only two

levels are used, however, the sequences are restricted to the

binary points.


C. The Noise Distribution


The noise present in the channel may also be given a geo-

metrical interpretation. Each coordinate of the signal vector is

affected independently by an additive noise n whose amplitude is

Gaussianly distributed. At any given signal point, each ni is

Gaussian with zero mean and variance N. The displacement of any

signal point from its original position due to noise can then be

considered to be a random variable n = nl, n2, nn, where each

ni is independent and has a one dimensional Gaussian distribution.









Cramer (10) shows that such a random variable has a probability

distribution of the general form


-I a jkn n
1 2A jk j k
p(n) = n2 e j (1)
(2fT) \7

where A is the determinant of the moment matrix

all aln 2)
A = (2)
anl ' ann

In this notation, ai is the variance of the ni, given by


ai = E(ni m)


and a is the covariance between ni and n., given by
ij

aij = E [ (ni mi)(nj- m )]

where mi is the mean of each ni and E is the usual notation for

average or expected value. By hypothesis, each m is zero

and each variance is N.

The form of the probability distribution of n can be

obtained by determining the probability distribution of the

standard variable t t2, t where t = (ni mi)/i

= ni/1' si being the standard deviation of n i

Then

a i= E N L = 1 E (n i) 1
11 W ^ ~N j


and










a = E = E(ni) E(nj) = 0, i # j,


since all the ni are assumed to be independent. The moment matrix

A then becomes the unit matrix and its determinant A is equal to 1.

In (1), all terms in the exponent vanish except those where j = k,

giving

-1 t2
1 2 i
p(t) = Ie i
M (2) n/2
(27T)

In n-space, the magnitude of a radius vector R from the
22 th
origin is given by IRI = r r where r is the ith component

of R. Let the magnitude of t be t. Then

1 2
1 I2 t
P(t = n/ e 2 (3)
(21T)

Thus, the probability distribution of the noise displacement is

independent of direction of displacement. For this type distribu-

tion, the contours of equiprobable surfaces are given by


1 3 A tt = C2
2A k Jk J k


where Ak is the cofactor of ajk in A. Since A (the determinant
jk jk
of A) = 1, and


,jk i = j

O, i / j











the surfaces of equal probability are given by

n 2 2
1 t = 2
2 ^ i
i=1


which is the parametric equation for a sphere in n-dimensions.

Hence (3) may be termed a spherical Gaussian distribution. The

probability distribution of displacement by noise in any given direc-

tion is independent of that direction and is a one-dimensional

Gaussian distribution.


D. The Decoding System


The encoding and communication process has been characterized

geometrically as the selection of points on the surface of an

n-dimensional sphere, which, in the transmission over the channel,

are displaced from their original location by noise that has a

spherical Gaussian distribution. A decoding system for such a model

is a partitioning of the n-space into M subsets corresponding to

the transmitted messages. This is a method of deciding, at the

receiver, which message was transmitted. If the received message

is in the subset corresponding to the ith transmitted code word,

then it is presumed that it was the ith code word which was sent.

For any code, the probability of error is defined as

M
P = piq
e i=1










where pi is the probability that the ith message will be trans-

mitted, and qi is the probability that if code word i is sent,

it will be decoded incorrectly, i,e., as a code word other than i.

For the purposes of this investigation, it is assumed that all code

words are equally likely to be transmitted, so that for a code of

M words
M
P Z q
e M ql
i=l


An optimal decoding system for a code is one which minimizes

the probability of error. The Gaussian density function is

monotone decreasing with distance. The greater the displacement

of a point from its original position, the less probable is that

displacement. With this noise distribution, an optimal decoding

system is one which decodes any received signal as being the code

word corresponding to the geometrically closest code word location.

This type of decoding is called minimum distance or maximum likeli-

hood decoding. This decoding system is assumed to be used through-

out the investigation reported in this paper.

One additional comment pertinent to decoding is offered.

As noted, the probability of error will depend on the geometrical

distance between code words. It would be possible, for a fixed

value of noise variance N, to decrease the probability of error by










placing the code words far enough apart in n-space. For a fixed N

and code length n, this would correspond to increasing the signal

level B. Loosely speaking, this is equivalent to increasing the

signal to noise ratio, which one expects to result in more reliable

communications. Thus, B will necessarily remain as a parameter

in error calculations,


E. Euclidean Distance as Related to Hamming Distance


A fundamental parameter of discrete codes is Hamming

distance, usually denoted by d. Hamming distance is defined to

be the number of digits in which two code words differ. For the

purpose of this investigation, a relation between Hamming and

Euclidean distance is required.

Consider two points in Euclidean n-space, wl = sl, s2 s,

and w = t i t t The distance D between these two points
2 1 2 n
is given by
2 n 2
D2 = (sr ti)
i=1

If these two points are binary points as defined in Part B above,
2 2
then (si t ) can have only the values 0 or 4B depending on

whether s = t, or s i t Now, w and w may also be considered
i i i i 1 2
to be code words. If the Hamming distance between these two words

is d, then si # t in d places. Hence





16




D2 = 4B2d

which is the necessary relation between Hamming and Euclidean

distance.















SECTION III


BOUNDS ON RELIABILITY


A. The Concept of a Bound


The evaluation of a particular coding technique for a

communication channel is ideally done by calculating the proba-

bility of error, Pe, for that technique. The Pe is more properly

written Pe(N,B,n,R) to show that error probability is a function

of noise power N, signal level B, code word length n, and informa-

tion rate R, Determination of an exact P may be impossible, or
e
mathematically quite complex. Consequently, it is necessary to

resort to bounds on P rather than an exact result. The bounds
e
usually derived on Pe are functions g which permit the inequality

gl Pe < g2


to be written. The functions gl and g2 can all be placed in the

form


-nE(R) + o(n)
e (1)

where R is a function of B and N. Here o(n) is a term of order

less than n.

This investigation is concerned with the use of group codes

as input signal sequences. For this class of codes, there are a

finite number of codes. Hence, there is a best code in the sense










that some code has a Pe which is no larger than any other code.

It is the Pe for this best code in the class of group codes that

will be bounded.

To further simplify the mathematical operations, this report

will be concerned primarily with determination of bounds on E(R),

which is called reliability. If the bounds developed on P are
e
placed in the form of (1), then E(R), or simply E, is defined as


E = lim 1 loge Pe
n--oo n


E is then independent of code word length n, which permits a

simplified presentation of results. E is a measure of how fast the

probability of error goes to zero. In this connection, it should

be noted that the exponent in the defining equation (1) for E is

negative. Hence, a lower bound on E will correspond to an upper

bound on P
e
Knowledge of E and n will not permit the close determination

of P from (1), since the term o(n) could be a large multiplier.

However, given E, and the P which is desired, the necessary value
e
of n can be determined fairly sharply when n is large. In fact,

n will be asymptotic to E loge P e In applications of coding

theory, this is normally the natural problem i.e., how long must the

code be to achieve a given level of Pe.










B. First Upper Bound on E(R)


Plotkin (11) has obtained a bound on minimum Hamming

distance which is applicable to binary codes in general and to

binary linear codes, which are a subclass of binary codes. This

bound on distance may be used as the origin of a bound on Pe in

binary coding for continuous channels. For this purpose, the most

convenient form of the bound is given by Peterson (12) as follows:

"Consider an n-symbol linear code with symbols taken from

the field of q elements. Let k be the number of information

symbols and (n-k) the number of check symbols. If


qd 1
nZ
q -

then


k S n + 1+ log d
q q


where d is the minimum Hamming distance between code words."

Since this investigation is concerned with binary coding,

q = 2. Then, for n 2 2d 1, the bound may be written as

k Sn 2d + 2 + log2 d

or

n k 2 2d log2 d 2. (1)

Future calculations will be simplified if the bound on

minimum distance as given by (1) is changed so that d does not

appear as the argument of a logarithm, Note that









d-1
d S 2

for any positive integer value of d. Hence

log2 d5 d 1.

Then, from (1)

n k 2d (d 1) 2 = d 1

so that

d 5n k + 1

This may be substituted in (1) to yield

n k 2d log2 (n k + 1) 2

or

dS= [n k + log2 (n k + 1) + 2 (2)


Equation (2) bounds the Hamming distance of a group code

in terms of code length n and the number of information digits k.

A more desirable form of (2) (for the purpose here) is one in which

transmission rate R or the number of code words M appears explicitly.

Rate, as used in the coding literature, is variously defined. A

common definition is that rate R is the ratio of the average number

of information symbols per code word to the average number of total

symbols per word (word length). Specifically, for an (n,k) group

code, rate is equal to k/n. Since there are M = 2k code words in a

group code, rate can also be written as 1 log2 M. Implicit in these
n
definitions, however, is the idea that rate as used in coding

theory is generally concerned with what a source (or code) is











capable of transmitting, rather than what it is actually trans-

mitting. For general use, a definition of rate should incorporate

this maximal concept. This may be done by using the concept of

self information.

Assume that there exists an ensemble of events X (such as

the ensemble of M code words), each of which occurs with a proba-

bility p(xi), where xi is the ith event. The self information of

x is defined as

I(xi) = -log p(xi).

The units of I are determined by the base of logarithms chosen.

Three commonly used units are bits for logarithms to the base 2,

nats for natural or naperian logarithms, and hartleys when the

base 10 is used. Self information may be interpreted as the maximum

amount of information that can possibly be provided about the

event x..
i
The desired definition of R is one which will specify condi-

tions under which the maximum average amount of information per code

word symbol is transmitted. The total self information of the

source is simply the sum of the self information of each event. The

rate R can then be defined as the maximum of

M
1 P(xi) I(xi).
n i=

If the definition of I is used, this can be written as

M
1=I p(x ) log p(xi) (3)
n fl L










which is recognized as the entropy function of information theory,

modified by the factor 1/n. It is known that the entropy function

achieves a maximum when all the p(xi)'s are equal (see, for example,

Reza (L)). Then p(x ) = 1/M for all i, and by hypothesis

M

i=1

Under this constraint, the maximum of (3) is


1 log = 1 log M. (4)
nl M n

Thus, if rate R is defined as


R = 1 log M, (5)
n

the desired maximization of average transmitted information is

achieved.

With the definition (5), the bound on minimum distance (2)

may be expressed as an explicit function of R, or of M, in the

following manner:


d S i 1 + log2 (n k + 1) + (6)
2 n n n

From (5),


R = In M nats,
n

where In indicates natural logarithms. For the group code,

M = 2k and










R =1 In 2k k In 2
n n

yielding

k R In M 1 (7)
n In 2 n In 2

The relation (7) is then used in (6) to give

d 2 n 2In 2 1 In M +1 In 2 log2 (n-k+1) + 2 (8)
2 In 2\ n n L[


as the desired bound on minimum distance.

In order to use (8) in a geometrical argument, Hamming

distance d is converted to Euclidean distance D by the relation

d = D2/4B2 developed in Section Ii. The final form of the bound

on minimum distance is then

DS; 2B2n In 2 I + In [ log2 (n-k+l) + 2 (9)
In 2 n n

The use of relation (9) requires that proper interpretation

be made of it. The bound does not guarantee that the minimum

distance between the code words in a group code can ever equal the

right side of (9). Rather, it only guarantees that the minimum

distance can never exceed the right side of (9). Specifically, (9)

says that in every binary code, at least one pair of code words is

no farther apart than the distance given. This fact is used by

Shannon in the determination of an upper bound on E for the unre-

stricted case.

Assume a group code with the maximum minimum distance. There

are two code words no farther apart than D as given by the right side

of (9). Call these two words wl and w2. If one of these words, say

wl, is transmitted, and maximum likelihood decoding is used, then the




24





contribution to the probability of error of the code if w1 is

incorrectly decoded as w2 is at least equal to the probability that

w is carried at least a distance D/2 towards w2. This contribu-

tion can be expressed as

1
M P (w moves at least D/2 in a 'pecified direction)


where 1/M is the probability that w1 will be transmitted. As

noted in Part C of Section II, the density function of displace-

ment by noise in any given direction is one-dimensional Gaussian

with zero mean and variance N. The contribution to the proba-

bility of error is then given by






S B In 2 In M + In 2 log2 (n k + 1) + 2]}


where

S(-x) = 1 -(x)

and
x
--2
e d
S(x) o= e dx .


Since the object here is a lower bound on the probability of

error P the contribution to error if wl is decoded as any
e









other word except w2 is neglected. This will result in an opti-

mistic value for P but this is the proper direction for reasoning
e
to a lower bound.

Assume now that this first word wl is deleted from the code.

Since there are now (M-l) code words, the maximum minimum distance

cannot be greater than that which would be possible in a code con-

taining (M-l) code words, i.e.,



D2n n In 1 n 2 n log2 (n k + 1) +
Fn n

Then, for the pair with this maximum minimum distance, the argu-

ment as used above will yield a contribution to the probability of

error of at least



B ( 2nn 2 1n 2- In (M- 1) + in1 log2 (n k + 1)+2
M 2N n2 n n 2 i

This process can be continued for (M-l) times, yielding a similar

contribution each time a code word is deleted. The last two code

words will yield a contribution of



1 ( /B 2n In 2 -1 In 2 +- n2 log (n k + 1) + 2
M p 2N In 2 n n 2

The probability of error is thus bounded as










pe L B n2 In 2 In M + In 2 [log (n k + 1) +2
e- M 2N In 2 n n L 2


S In 2 In (M-) + n- g2 (n-k+l) + 2])

(10)



+B2n In 2 In 2 + In 2 log2 (n k+1) + 2}) .
\ V2N In 2 n n



This expression may be simplified somewhat by weakening the bound.

Note that the terms on the right side of the inequality are decreas-

ing in value because each argument is greater than the preceding

one and for x 'y, < (-x)c=_ < (-y). Discard the last (M/2)-l terms

and replace the first M/2 terms with the last remaining term. This

last remaining term is



1 n B2 2 -1 In M + In 2 log2 (n k + 1) + 2]
M 2N In2 n 2 n 2

Then


(P 2 2/ B2n In 2 -1 In M + In 2 flog (n k+ 1) + 2 .
e 2 2N In 2 n 2 n I 2

(11)
Feller (14) shows that 4 (-x) is asymptotic to
2
x
1 "--
e (12)
x-V rF









Since the primary concern here is for reliability, which implies

very large n, this asymptotic result can be used. If (12) is

applied to (11), there results

2 f
B2n In 2 In M + In 2 g (n k + 1) + 2]

Pe e
4N In 2 \ n 2 n 09 2 J

Sn In 2 1 In + In 2 0log2 (n -k + 1) + 2 1
N In 2 n 2 n
(13)
To obtain reliability, the definition

E = lim - in P
n e
n -oo

is applied to (13):


- 1 In P 1 -Iln in 2 Iln 2 i In + n2 lg2 (n-k+l) + 2]
n e n N In 2 n 2 n L 2 ]


B2 n In 2 -1In I+n- 21og (n-k+l) + 21
4N In 2 n 2 n L 2 +


- 1 In P 5 B2 ln 2 1 In M + 1 In 2 + In 2
n e 4N In 2 n n n



[log2 (n-k+1) + 21


+1 in 4B2nL In 2 I In + n 2 [log2 (n-k+1) + 21}


n
equal to rate R. In the limit, those terms which have as a factor

1/n will vanish to give










E B2 1 R (14)
4N In 2


This is the first upper bound on E.

It is noted again that the Plotkin bound (1) is applicable

to binary codes in general and thus to binary linear codes which

are a subclass of binary codes. Hence, the bound on reliability

given by (14) applies to both and so may be considered to be a

somewhat stronger result than a bound for binary linear codes

only.


C. First Lower Bound on E(R)


The first lower bound on E(R) will be derived from an upper

bound on Pe. The distance structure of group codes is again needed,

but for an upper bound on P a lower bound on minimum distance will

be required. Because of the monotone decreasing nature of the Gaus-

sian density function, a lower bound on minimum distance in a group

code will determine the maximum contribution to Pe from any two code

words. The sum of the contributions to Pe from all code word pairs

will then yield an upper limit on Pe provided that the summation is

done in an optimistic direction for an upper bound. This, in brief,

is the outline of the method to be used for the derivation.











The starting point for the derivation of the upper bound

on P is a bound on minimum distance for binary codes first found
e
by Gilbert (15) and refined by Varsharmov. A convenient statement

of the bound for group codes is given by Peterson (16) as follows:

If n + d
nH
2 n -k 2 2 n 1 (1)
2 2 (1)

where n = total number of digits per code word, k = number of
information digits per code word, and H2(x) = x log2 x
(1 x) log2 (1 x), then there exists a binary (n,k) group
code with minimum Hamming distance d."

The entropy function H(x) is symmetrical in x and (l-x), i.e.,

H(x) = H(1-x). Then


2 n+- ) = 2 (d 2

and (1) can be written



2n k 22 (n-2) (2)


The inequality (2) is in the wrong direction for the desired

lower bound on minimum distance. Let k be the maximum value of k

for which (1) holds. Then there exists a code with k information
m
symbols and distance d. Since k is chosen to be the maximum, (1)
m
does not hold for k = (km + 1), so


2n-(km+1)< 2nH2 ( n- (3)

The inequality (3) is now in the desired direction. It can be

further manipulated to arrive at a more suitable form as

follows:











d -2
n k 2 n l-
2 2





-k d- n1H2 ( )] -1

2 z. 2(4
-kd 2

2 <= 2

orn [- H d 1

2 =k 2 (4)


where the subscript on k has been dropped. The theorem statement

of (1) guarantees the existence of an (n,k) group code such that (4)

is valid. For group codes, the number of code words M = 2k. Hence



Md d- 2 (5)
d

where Md denotes the number of words with minimum distance d. Equa-

tion (5) is interpreted as a lower bound on the number of words in

an (n,k) group code with minimum distance d. This bound may also

be expressed as a lower bound on rate, since


R = In M
n


giving

nR
e = M,

which, when placed in (5), yields











n[ - 1
[1 2 2)
enR 2 (6)

from which is obtained the inequality


R > d( -1 1 In 2. (7)


The quantity (d 2)/(n 1) will approach d/n for large n and d,

and the factor 1/n will become negligible, so that (7) may be

written

R> [I H2 (1 1 In 2

or

R> In 2 He ( (8)
e n

where H (x) indicates that the logarithms are to be taken to the
e
base e. The relation (8) may be expressed in terms of Euclidean

distance as


R > In 2 H (9)
e 2
e LB n J

The bound (5) establishes that, in an (n,k) group code,

there are at least Md words which are separated by distance d

where M is given by the right side of (5). An upper bound on P
d e
for such a code can be obtained by adding up the probability that

each code word will be decoded as each of the remaining code words.

Consider that word wl is transmitted. Since maximum likelihood

decoding is used, the probability of error if wl is decoded as,










say, w2, is the product of the probability that w1 is transmitted

(1/Md, since all words are equally likely) and the probability that

w1 moves at least half way towards w2. Since it is known that w2 is

at least D distance from wl, then this latter probability is no

more than Q (-D/2}\J). Note that it is here that the lower bound

on distance is used. The word w2 could be farther away than D,

but in that event the probability of wl moving halfway towards w2

would be less. Hence, the most pessimistic case is taken. The con-

tribution to P by w being decoded as w2 can then be written as
e 1 2


Md 2V- ]

There are at least Md code words, each of which could be paired

(as an ordered pair) with (Md 1) other code words. Hence


Pe S Md (Md 1) 2- -


The inequality is preserved and simplified if this is written as


P S M! D .d (10)


Since
nR
Md = e

(10) may be then written

nR D
SI- (11)
e 21N










As noted earlier, ( (-x) is overbounded by

2
1 e 2
xV F-

This may be applied to (11) giving

D2
2 VN 1 enR 8N
pZ N e e
e D V

which simplifies to


D2
nR -
2N 8N
P e e (12)
e D2 T

It is apparent from (12) that the tightest bound will occur when

R as given by the right side of (9) takes on its minimum value.

The inequality (12) is an upper bound on Pe with D as a

parameter. It will be more convenient to express D as a function

of n by use of the relation

D = XK(n), (13)

where K(n) is the maximum value which D can assume. This maximum

value can be determined from the Hamming-Euclidean distance, relation,

D = 2B -d

The maximum Hamming distance for an n-digit code length is thus con-

strained to be 2B'\n. However, in Section IV of this paper, a

comparison between these results and those of the unrestricted theory

will be made. In developing a similar bound for the general case,









Shannon found it expedient to restrict the maximum code word separa-

tion to V2Bn, which is less than the maximum by a factor of V1.

This restriction in effect is a lower bound on rate and permitted a

simpler bounding technique. In order to make a meaningful comparison,

the maximum code word separation for the binary case will be assumed

to be the same. Then, K(n) = 2Bn, and


D = 22B 2n (14)

The equation (12) can be written, using (14), as


__ n nR 2B2
p i n e 4N (15)
e X e2

Reliability, which is the asymptotic (as n-.oo ) value of the nega-

tive of the exponent in (15), is then bounded as


E X2B2 R .(16)
4N

This is the first lower bound on reliability.

If the relation (14) is used in (9), then R is bounded as


R > In 2 He (17)


The equations (16) and (17) can be used to determine E(R) through

k as A varies from 0 to 1.

It should be pointed out here that the specialization of a

lower bound on reliability to group codes is desirable. Group

codes are a sub-class of the class of all binary codes. Thus, if










a group code exists which will give this reliability, certainly a

binary code (the same code) exists which will do as well

The bound obtained by the argument which was used in this

section is sometimes confusing because of the simultaneous bound-

ing of M and Pe An over-all interpretation may be of help in

understanding what has happened. The basic relation for the bound

on P is
e

P e d- 2"^N

This says that the true value of Pe is less than the quantity on

the right and that the quantity on the right increases directly as

Md. Call this quantity on the right of (10) P Further, Md is

bounded from below by (5) as

n[ d 2
n2[ (- 2 1
Md > 2


Call the quantity on the right M and note that M is a function

of d. Figure 1 shows P plotted as a function of M The point
8 g
labeled d on the curve in Figure 1 shows a particular bound value

for P when a value d is used to calculate M Now, P S P
g o g e g
says that the true value of P must lie below the point d and
e o
Md := M says that the true value of M is to the right Since
g d
the curve slopes up to the right, the true value definitely lies

below the curve. Point A is a lower bound on M for a fixed value

of Pe, and point B is an upper bound on Pe for a fixed value

Md of M.
d









D. Second Upper Bound on E(R)


In the theory of discrete codes for binary symmetric

channels, an optimum lower bound on the probability of error has

been clearly established. This bound is known variously as the

Sphere-Packing Bound or Hamming Bound. The bound is due originally

to Hamming (17) and has been restated in more modern and convenient

form by Peterson (18) and Fano (19). Briefly, the bound states that

the probability of error for the best possible (n,k) code can be no

smaller than that for a hypothetical code called a quasi-perfect

m-error correcting code. A quasi-perfect code is one which corrects

all combinations of m or fewer errors, some of (m + 1) errors and no

combinations of greater than (m + 1) errors. The term hypothetical

is used because such a quasi-perfect code may not exist for all combi-

nations of n and k.

Peterson (18) has stated the Hamming Bound as an upper bound

on minimum distance for an (n,k) group code. It should be noted that

the bound actually applies to all binary codes. This bound is given by


1 h H2 (:i (1)


where n is code word length, k is the number of information digits

in the code word, m specifies the number of errors which the code

guarantees to correct, and H2(x) is the binary entropy function.

A change of base in logarithms, plus the application of the general

definition for rate, yields









In 2- n In M 2: He ( (2)


where M is the number of words in the code.

If a code is an m-error correcting code, then the minimum

distance is at least given by

d = 2m + 1,

which gives

d 1
m =
2

or, for large values of d,


m =d (3)
2

If the Euclidean-Hamming distance conversion is applied to (3),

the bound (2) may be written


In 2 1 In M ; H D (4)
n e 8B2n

The argument to be used for obtaining a lower bound on

error probability is a geometrical argument similar to that used in

Part B of this section. However, the bound on distance (4) is

transcendental, and no explicit solution for D in closed form is

attainable Rather than attempt to solve (4) by approximation or

by numerical techniques, an "inverse function" approach will be used.

From (3), the value of the argument of H is d/2n, approx-

imately. Since d is Hamming distance, the maximum value of d is

n, i.e., two code words can differ at most in every position. Thus,

the maximum value of the argument of H is 0.5. H (x) is monotone
e e









increasing over the range 0 S x S 0.5. Hence, it is single

-1
valued over this range, and an inverse function H (x) can be
e
-1
defined. H (x) is a number y for which H (y) = x. Using this
e e
inverse notation, the bound (4) may be expressed as

2 -1
D S H (In 2 1 In M) (5)
8B2n e n

or 1

DS 8Bn H-e (In 2 1 n M) (6)
Se n J

-1
Now, H is monotone increasing because H is. Hence, as the value
e' e
of M in (6) decreases, the bound value of D will increase. There-

fore, the same technique as was used in Part B of this section, i.e.,

the determination of contributions to the probability of error by

successive deletion of code words at maximum minimum distance and

subsequent modification of the distance expression can be used

now on (6) to obtain a lower bound on P These calculations are
e
straightforward and yield as a bound



p -1 4> 2B2n H-1 (In 2- R +1 In 2) (7)
e 2 V N e n

When the asymptotic approximation of 4(-x) is applied, there

results

He- (in 2 R + n 2)
e n
Pe (8)

4./ nV -1 1
2 H1 (in 2 -R + n 2)
e e n
The established definition for reliability will yield











E 5 B2 H- (In 2 R) (9)
N e

The relation (9) is the second upper bound on E(R). As was

true in the case of the first upper bound, (9) is actually

applicable to all binary codes.


E, Second Lower Bound on E(R)


Kennedy and Wozencraft (20) have established a random coding

bound for discrete, memoryless channels. This bound is an outgrowth

of work by Fano and Gallagher. It states that it is possible to

code and decode data for such a channel with a probability of error

bounded by


P S 2-n(Eo R) (1)

where n is the code word length, R is the rate in bits per second,

and E is the reliability obtained when only two code words are in

the message ensemble for the channel, i.e., M = 2. A brief outline

of the argument leading to this bound will be given, since the method

of Kennedy and Wozencraft was adapted for the derivation of a similar

bound for the time-discrete continuous channel.

Suppose that, from the set of n-tuples over the field of two

elements, two code words are chosen at random. Define the probabil-

ity of error if either of these words is transmitted as

-nEo
P = 2 o










Now, choose M words at random. The probability of error if the

first (or any one) of these is transmitted is

P = P (The received message is closer to word 2 than
e word 1, or, the received message is closer to
word 3 than word 1, or, .)

The probability of a union of events is overbounded by the sum of

the probabilities of the component events. Hence

P S P (received message is closer to word 2 than word 1)
e

+ P (received message is closer to word 3 than word 1)



But each component probability is the probability than an error

will occur in the transmission of two randomly chosen code words,

since all code words were chosen at random. Thus


P -S (M 1)2"Eo M2-nEo (2)

Rate R in bits is given by


R = 1 log M.
n 2

Then nR
M= 2
and (2) can be written

-n (Eo R)
P_ <= 2


which is the Kennedy-Wozencraft bound. The customary definition

of reliability gives

E 2 E R. (3)
o









Note that defining the probability of error of transmitting one of

two randomly chosen code words as


-nEo
Pe = e


will result in the same expression, (3), for reliability, where R

will then be in natural units.

A method will now be given for obtaining a bound on Eo which

will result in a random coding bound for the time-discrete channel

with + B as input signal levels. This approach removes the restric-

tion that the input signal sequences form a binary linear code. The

bound will apply to the entire class of binary codes, i.e., codes

selected from the complete vector space of n-tuples over the field

of two elements. This method requires a simplified bound on 4 (-x)

and an evaluation, for the time-discrete channel, of the probability

of error for two randomly chosen code words.

It can be shown that

X22
22
(-x) e O2 O x oo (4)


in the following manner:

Let

x2
D(x) = <(-x) e 2


then, the inequality (4) is valid if D(x) S 0 over the indicated

range. By direct subsitution, D(x) = 0 when x = 0, and when

x =oo Now,








x2
dD = e 2 ( x 1 (5)
dx 2

which has only one root at x = 2/ -j-27 Also, dD/dx exists over

the entire range. Thus, D(x) has only one relative extremum and no

crossings between 0 andoo A test of any sample value shows D(x)

is negative in the indicated range; hence, D(x)S 0 for 0 S xoo .

Consider that two binary code words are chosen at random.

If these two words differ by a distance d, then, still assuming a

binary signal level + B, they are separated by Euclidean distance

D = 2B.\d. If one of these is transmitted, the probability of

error in decoding is no greater than the probability that one of

them is moved by noise at least D/2, i.e.,


Ped = -B

or

B2d
P 1e (6)
ed 2

where (4) has been used.

Since each code word is chosen at random, the probability

of choosing any one is 2- There are (n) words which will differ

in d places from the first chosen. Thus, the probability that the

two code words differ in d places is


2-n n
d)

The average probability of error in two randomly chosen code words

is then (6) averaged over all possible d, i.e.,











n

Pe T
o d=o


2-n p)


1
S 2



1
o 2





P i ( -+e
e 0 2
o


B2d
e
2


B2
-d
2N


n n

0+ ed




1 + e


By definition,


P -nEo
P =e 0
eo
when this is used in (7), there results


-nE, < 1 + e" n
e 2


E 2 In
o


B2
l+e 2N
2


The inequality (8) may be applied in (3) to give the second

lower bound on reliability as


[ In- 2) \
In I + e 2N +
2














SECTION IV


COMPARISON WITH THE UNRESTRICTED THEORY


A. Preliminary Remarks


Prior to a comparison of the results of this investigation

and the unrestricted theory of Shannon (4), a summary and explana-

tion of the unrestricted bounds is in order. It should be remem-

bered that in the unrestricted case the channel input sequence is

considered to be n real numbers, subject only to the constraint

that the power P in each sequence of n numbers be identical.

Figure 2 depicts the four unrestricted bounds on reliabil-

ity plotted as a function of rate R, with the parameter A = (P/N)1/2

= 3. The power P will correspond to B2 in the binary coding case.

N is as before the average noise power, For ease of discussion the

bounds have been labeled E1 through E 4

Bound E1 is the bound on reliability when P is obtained

by the sphere-packing method. The quantitative expression for this

bound is complex, but it will be recorded here for interest.

E l A2 1 AG cos 9 In (G sin 8) (1)
2 2

where

G = 1 (A cos 9 + A2 cos2 9 4 ) (2)
2

and 9 is a function of rate through the expression

e-R = sin 9 (3)
e =sin 8 (3)









The bound (1) is valid for ranges of 9 greater than go, which is

the value of 8 which corresponds to channel capacity, i.e., for

: o = sin-1 (1/ V + A2). When 8 = 90, then E1 = 0, which is

the correct value of reliability at channel capacity. E1 represents

the highest possible reliability attainable with the unrestricted

coding scheme. However, its derivation is based on all possible

codes which could be formed. It provides an upper bound on relia-

bility for all ranges of R, but at low values of R it is found that

this bound cannot be achieved.

The bound E3 is an upper bound on reliability which is

sharper than E1 at low transmission rates. E3 is independent of

rate, and is given by

P P A2
E A(4)
4N 4

The lower bound E2 is one which is obtained when P is cal-

culated using a random coding technique. E2 is actually given by

two different expressions, according to whether or not R is greater

than or less than a value called critical rate, Rc. The concept of

critical rate occurs in coding theory whenever general derivations

of upper bounds on Pe are obtained with a random coding argument.

It is critical only in the sense that the nature of the bounds for

P and E are different for ranges of R on either side of R When

R => Rc, the asymptotic lower and upper bounds on Pe differ only by

a multiplying factor which is a function of rate. Thus, for





46




R > R the reliability E, which is an exponent, is the same for
c
both bounds on Pe. In this range, then, E2 is exactly the same as

E1 and is given by (1). For rates less than Rc, E1 and E2 diverge.

In this range, E is given by

E2 E- EL(c) + (Rc R) (5)


where


A2
EL(c) A AG cos e In (G sin 9 )
EL d 2 2 c c


is the value of E1 at the critical rate, G is given by (

9 is a function of critical rate through equation (3).
c
of 9 is the solution of 2 cos 9 = AG sin2 9 Despite
c c c
complexity of the equations required to determine EL(9c)

the bound E2 is linear over the range 0 S R R .

For low values of R, E2 is sharpened by E4. The

is given by


E R
4 4N


2), and

The value

the

and R


bound E4


where


R = In sin 2 sin-
I\ -2


_= D
V2Pn










If B2 is substituted for P, E4 has the same form as the first lower

bound on reliability as developed in Section III, Part C, of this

paper. However, the expression for rate R is considerably different.

The curves in Figure 2 give a typical picture of the bounds

obtained by the unrestricted theory For rates less than the critical

rate R the bounds enclose an area within which the reliability
c
must lie. For rates greater than R the reliability is given by a
c
single curve. Thus, for rates near zero and rates near to or greater

than Rc, the reliability is determined fairly sharply.

All the bounds on reliability, as typified by Figure 2,

are derived for the best code which the unrestricted technique can

achieve. This fact requires that a slightly different interpretation

be made of upper and lower bounds. Bounds E1 and E3 say, in effect,

that regardless of what type of code is attempted, within the

limits set by the unrestricted coding technique, no higher relia-

bility can be achieved. The best that can be done is to meet these

bounds. Hence, it is expected that a restrictive coding technique

such as that used in this investigation may fall short of these

bounds. The lower bounds on reliability, E2 and E4, are the result

of a random coding technique. This technique results in the following

reasoning: assume that each code is formed by selecting at random M

code words for each code out of the total ensemble of possible

message sequences. The average probability of error for all such

possible codes is then calculated. Since, for all such codes, the

average probability of error is then known, the best code must have

a probability of error which is at least average. This is the key









point in random code bounding. It is asserted that there exists

a code which will give a probability of error and hence a relia-

bility at least as good as the average. There is no indication of

how this code is to be found. The binary technique is a special

case of the unrestricted technique. In evaluating reliability for

this case, the question being asked is, in effect, is this one of

the codes capable of achieving the random coding bound.


B. A Comparison of Upper Bounds


Figures 3 through 8 show the first and second upper bounds

on reliability plotted as lines A and B, respectively. These bounds

are plotted against rate for various values of the parameter
2 1/2
A = (B2/N) 2, The first upper bound is given by

2
E A (1 R )
4 In 2


The second upper bound is given by


2 -1
ES A H1 (In 2 R).
e
The two bounds may be considered to have different ranges of

validity. For RS 0.25 the first upper bound is lower and hence

predominates. For R 20.25 the second upper bound is lower and

is thus the prevailing one.

For all values of A, these bounds are lower than the low

rate upper bound for the unrestricted case. For values of A

greater than 3, approximately, these bounds are also lower than

the sphere-packing bound for the unrestricted case. It is in this










range that the derived bounds give the greatest information. It

is seen that as A increases beyond 3, the reliability of the binary

code is bounded away from the optimum, represented by the sphere-

packing bound. The bounds show clearly that the binary coding

technique is restricted to a maximum signalling rate of In 2.

For values of A less than 3, the upper bounds convey less

information. In this range of A, the first upper bound lies under

the sphere-packing bound for low ranges of R and intersects that

bound at a value of R always less than Rco The second upper bound

lies above the sphere-packing bound except at very low R and at

R near In 2. The amount of definite information given about the

binary technique for small A is less than that available for

higher A.

Where the sphere-packing bound is higher than the first and

second upper bounds, the difference between them may be used as

a crude measure of the loss in reliability resulting from the use

of the binary technique. It is not too accurate a measure because

there is no guarantee that the first and second upper bounds can be

met, that is, that a code can be found which will achieve this

reliability, while with the unrestricted technique, the optimum

bound could be very closely approached, if not actually reached.

To do so might require that many levels of signal quantization be

available, but this is, in theory, no barrier.










In all situations where the first and second upper bounds

convey information, it indicates that the binary coding technique

can give, at best, less reliability than the optimum unrestricted

code. The degree of this degradation increases as the signal to

noise ratio increases. In all cases, the situation is not too sharp

at very low rates. Each figure shows that the sphere-packing

bound, which is of exponential shape, is sharply truncated by

the low rate bound for the unrestricted case. The resulting

unrestricted upper bound does not appear to be a realistic or

naturally achievable limit. A reasonable estimate is that the

true upper bound lies between the first upper bound for the binary

case and the truncated unrestricted bound, in which case the binary

case will not be bounded as far away from the optimum.

The second upper bound was derived from the Hamming distance

bound for group codes A group code for the Binary Symmetric

Channel which meets the Hamming bound is optimum, i.e., no code

can be found with the same n and k which has a lower probability

of error. It has not been established that the second upper bound

is optimum for the channel investigated herein. However, the

second upper bound does serve as a guide to the performance to be

expected when an optimum group code is used in the time-discrete

channel.











C. A Comparison of Lower Bounds


1. The First Lower Bound


The first lower bound is plotted as curve C on Figures 4

through 8. The bound was derived in terms of minimum distance D

between code words, in the same manner as was the bound for the

unrestricted case. The expression for E is the same for both

and is

E R,
4

where

= D



Rate R is a function of X, but the function is not the same for the

binary and unrestricted cases. For the binary case, R is given by


R 2 In 2 He ( )


This can be compared with R for the unrestricted case as given in

Part A of this section. To present E as a function of R, several

auxiliary curves were used. Figure 9 plots rate as a function

of for both cases. Figures 10, 11, and 12 give E as a function

of X for various values of A.

The first lower bound lies slightly under the low rate

bound for the unrestricted case. For lower values of A, it coin-

cides with the unrestricted bound at low rates. It would be










unreasonable to expect that the binary coding technique could give

a higher reliability than that of the unrestricted case. It must

be remembered that the binary case is only a special case of the

unrestricted coding scheme. If the binary technique could guarantee

that codes could be found whose reliability was no worse than

some value K(R), and K(R) was better than the corresponding bound

for the unrestricted case, then the unrestricted bound could be

raised to match, since the binary group codes are one of those avail-

able in the unrestricted case. It is felt by the author that the

close proximity, at low rates, of the lower bounds of the binary and

unrestricted schemes is significant when the highly restrictive

nature of binary coding is compared with the unlimited numbers of

codes available in the unrestricted case.


1. The Second Lower Bound


The second lower bound is plotted as line D on Figures 4

through 8. This bound lies under the unrestricted random coding

bound for all values of A. Its separation from the unrestricted

bound increases as A increases. For small A, it approaches the

unrestricted bound quite closely. The departure of the second

lower bound from the unrestricted random coding bound can be used

as a much better measure of the loss of reliability by binary coding

than could the first upper bound. The unrestricted random coding

bound says that, from all the codes available, there can be





53




selected one or more which can have no lower reliability than is

given by the bound. The second lower bound guarantees that

among all the binary codes available one or more exists which can

have no lower reliability than that given by the bound. Since these

bounds are not widely divergent, and in fact, are quite close at

the lower values of A, the curves show that binary codes can be

found which are not significantly worse than the average code for

the unrestricted case.













SECTION V


CONCLUSIONS


A. Summary of the Investigation


The stated purpose of this investigation was to inquire into

the use of binary codes, particularly binary linear codes, as input

signal sequences for a time-discrete continuous channel. More

specifically, it was desired to determine if binary codes as a

class were so inferior in performance that a different technique of

coding should be sought or if their performance approached the unre-

stricted codes closely enough to warrant a more detailed study. The

method of this inquiry was to determine the reliability of such codes

for this channel and to contrast this with the reliability derived for

the more general unrestricted case by Shannon. The reliability deter-

mination was done by the derivation of two upper and two lower bounds

on reliability for the binary coding technique.

The first and second upper bounds demonstrate that, for a

substantial range of the signal to noise ratio, the upper limit of

reliability of the binary coding technique is bounded below the

optimum reliability for the unrestricted case. This is not an unex-

pected result The value of the upper bounds is that they are an

analytical verification of the expected result. The plotted curves

show that the binary method is bounded away from optimum by a

fairly large amount in most cases.

54










The lower bounds on reliability are the most surprising and

significant results of the investigation. The first lower bound is

valid for low rates of information transmission. This bound at worst

lies only slightly below the corresponding bound for the unrestricted

case. It guarantees that binary linear codes exist which give, at

worst, a reliability only slightly less than that guaranteed for the

unrestricted case. Any lower bound on reliability is, in a sense, the

only positive statement which can be made about the merit of a code.

The close proximity of the lower bounds for the binary and unre-

stricted cases indicates that, by the use of binary linear codes, a

value of reliability can be guaranteed which is only slightly worse

than that which is guaranteed if the entire ensemble of codes avail-

able in the unrestricted case is used.

The second lower bound is valid over higher ranges of rate

than is the first lower bound. It, too, lies beneath the unrestricted

bound. The divergence of the two bounds is somewhat greater than is

the case for the first lower bound. The statements about the first

lower bound apply here also, except that the difference in guaran-

teed reliability between the binary and unrestricted cases is greater

over the range for which the second lower bound is valid. It should

also be emphasized that the second lower bound is not derived for

binary linear codes, but rather for binary codes in general It is

the only one of the bounds obtained which has this exception.











Overall, the use of binary codes appears to be a promising

method of coding for the time-discrete continuous channel, provided

the signal-to-noise ratio is not too large. As indicated by the

various figures, the bounds on reliability for the binary case become

increasingly divergent from those of the unrestricted case as the

signal to noise ratio increases. For larger values of A, non-binary

discrete codes would probably yield results much closer to those of

the unrestricted case.

The bounds derived for this investigation are the only indi-

cations available (to the best of the author's knowledge) of what

is possible when a class of discrete codes is used in a time-discrete

continuous channel. It is reasonable to expect that when the input

signals are restricted to a comparatively tiny subset of the total

ensemble of codes available to the channel, the reliability will be

decreased. It is, however, encouraging that the lower bounds for the

binary coding technique can guarantee at lower signal-to-noise ratios

a reliability which is close to that guaranteed by the unrestricted

codes, over a reasonably wide range of transmission rates.


B. Suggestions for Future Work


This investigation has shown that the use of binary codes as

an encoding technique for the time-discrete channel is promising. It

can be reasonably conjectured that even better results could be

achieved by the use of non-binary discrete codes, which would make










available additional levels of signal quantization. The possible

problems for future work are numerous. They fall rather naturally

into two groups, the continuation of research in the vein of this

paper and studies directed toward the application of discrete codes

to actual information handling systems.

The first theoretical investigation might well concern itself

with a sharpening of the bounds for the binary case. A most informa-

tive result would be an optimum or least upper bound, This would pin

down the maximum capability of this type coding. The lower bounds

herein were obtained, in some cases, by comparatively crude techniques.

It is, however, questionable how much of an improvement can be obtained

in any reasonable fashion.

A most fruitful area for future work would be the use of non-

binary linear codes, i.e., codes from ternary, quarternary, and in

general, q-ary systems. As additional levels of signal quantization

are made available the bounds on reliability, and in particular the

upper bounds, would be expected to approach the unrestricted case,

since, in effect, an infinite number of signal levels are available

in the unrestricted case. A measure or technique for determining

how many levels are required to approach the unrestricted bound to

within some specified interval would be the ultimate result one

might expect.

When the actual application of discrete codes is considered,

several problems are immediately apparent which warrant study. The




58




need for decoding methods is clear, and an investigation to determine

them would be challenging. The implementation of a maximum likeli-

hood decoder does not appear to be practical. On the other hand,

digit-by-digit decoding will penalize the reliability. Is there an

effective compromise? Finally, in the application of discrete codes,

some code must be selected. An investigation of specific codes for

use in this application would be informative.


























P vs. M










true value for d


Interpretation of First Lower Bound


o/I
I
I
I
I -
I


Figure 1.

























A = = 3


0 0.2 0.4 0.6 0.8 1.0


Rate R


Figure 2. Bounds on Reliability--Unrestricted Theory


5.0





4.0






3.0





2.0






1.0






0


. E1














- Unrestricted Theory Bounds

Binary Bounds


A = First Upper Bound

B = Second Upper Bound

D = Second Lower Bound


0 0.02 0.04 0.06 0.08
Rate

Figure 3. Comparison of Reliability for A = 1/2


0.10


m 0.14





0.12






0.10






0.08






0.06






0.04






0.02






0

















0.6


-- Unrestricted Theory Bounds


Binary Bounds


0.5 A = First Upper Bound

B = Second Upper Bound

C = First Lower Bound


0.4 D = Second Lower Bound







S0.3 B
-4
1-4

-4


--4
0.2-







0.1









0 0.1 0.2 0.3 0.4 0.5
Rate
Figure 4. Comparison of Reliability for A = 1





















S Unrestricted Theory
Bounds

Binary Bounds

A = First Upper Bound

B = Second Upper Bound

C = First Lower Bound

D = Second Lower Bound


0.6 0.8


Rate

Figure 5. Comparison of Reliability for A = 2


2.4






2.0






1.6






1.2
4.J
'4
1-4



0.8







0.4






0






















Unrestricted Theory
Bounds

Binary Bounds

A = First Upper Bound

B = Second Upper Bound

C = First Lower Bound

D = Second Lower Bound


0 0.2 0.4


0.6


0.8


Rate

Figure 6. Comparison of Reliability for A = 3


5






4






3






2

4O



1






0























8


-- Unrestricted Theory Bounds


6 \ -- Binary Bounds

\ A = First Upper Bound
B
B = Second Upper Bound

4 C = First Lower Bound

D = Second Lower Bound
*-I



S 2





0 D\

0 0.4 0.8 1.2 1.6

Rate

Figure 7. Comparison of Reliability for A = 4





66



35

-- Unrestricted Theory Bounds


30 Binary Bounds
A = First Upper Bound

B = Second Upper Bound

25 C = First Lower Bound
D = Second Lower Bound



20
B
"\


S 15 \










5
C
_



D -
0
0 rr r -I ---- I --- ~" ~ -----

0 0.4 0.8 1.2 1.6 2,0

Rate

Figure 8. Comparison of Reliability for A = 8














A = Unrestricted Case

B = Binary Case


0.2 0.4 0.6


Figure 9. Comparison of Rate vs.2n
Figure 9. Comparison of Rate vs.


0.8


1.4






1.2






1.0





0.8


0.6






0.4





0.2





0.


















Note: For each value of A
the upper curve is the binary
case; the lower curve is the
unrestricted case.










A= 3


First Lower Bound on Reliability, A = 1/2, 1, 2, 3


2.5






2.0






1.5






1.0


0.5





0


0 0.2 0.4 0.6 0.8

X = D/ 2nP


Figure 10.














A = Binary Case

B = Unrestricted Case


3.5






3.0





2.5-






2.0-






1.5 --






1.0






0.5 -





0


0.2 0.4 0.6 0.8

e = D/ n R A

Figure 11. First Lower Bound on Reliability, A = 4


B
--T- - ----- V


0


1.










14 -




B, D = Binary Case
12
C, E = Unrestricted Case





10 -


A = 16


8


SB
C

4 6





4

A= 8



2 D





0

0 0.1 0.2 0.3 0.4 0.5

SD/ 2nP

Figure 12. First Lower Bound on Reliability, A = 8, 16



































APPENDIX













APPENDIX


ALLIED RESULTS


A. Minimum Power Theorem


Suppose that there exists a set of M signals, each of which

is represented as an n-dimensional vector in n-space in a primary

coordinate frame S. Thus the ith signal will be

xi = Xil, xi2, x ij xin


Each x j is a voltage, which could be derived from some sampled value

of a more complex signal representation. For simplicity, assume that

each signal component xij has a duration of 1 second. The power in

the ith signal is, with respect to frame S, equal to
n
1 2
n L. ix
j=l

The average power P of the ensemble of M signals is then


M n 2
P p- n x
j=l
where pi is the probability that the ith signal will occur, and

M
Pi= 1.





73



If the signal points remain fixed with regard to the primary

coordinate frame S, then the average power will be a function of the

position of the coordinate system in which the power is calculated,

for assume that the power is calculated with respect to a new coor-

dinate system S centered on K = k, k2, kn. Then
1, 2, n
M n


i j=l

where

xi = (xij k)


thus
M n
P p ( x k) 2 (1)
P = Pi n 1 (xij kJ)2 (1)
j=1

which is clearly a function of K.

We wish to know if there exists a meaningful value of K

which will minimize P. Consider a mechanical analogy. Let each

signal vector xi be the radius vector to a point of mass mi, where

mi = M, and p mi/M. Then, the center of mass of the system

of mass points is given by

M

m ix
R = (2)
M

Thus R = rl, r2, r., r









where


r =1 M
j m x
S M i ij


dP/dK =

tion of

dP/dK =


M
r = Pp x(
j i1 i ij

Taking the derivative of (1) with respect to K, we get
M n
i pi (i/n (-2)(xij k )) since p is not a func-
i=l j=l ij i
2 -2
K. The second derivative d P/dK is positive, hence

0 will yield a minimum. Then


M n
-2 pi ( 1 (x.
i= n j=l 1

which requires
M n
pi (xij k )
I=1 j=l


- k )) =0
J


= 0


and


M n

S E Pi(xij k) = 0
i=l j=l j

Interchanging the order of summation, there results

n M

S E Pi(Xj kj) = 0
j=l i=l

This will be satisfied if

M
SPi(xij k ) = 0

for each Since the sum is on (5) may be written
for each J. Since the sum is on i, (5) may be written









M M

SPixj Pkj = 0
i=1 i=l
M
Recall that pi = 1, thus the condition


M
SPixj = k (6)
i=l

for all j will minimize the average power in the signal. But (6)

is equivalent to (4). Thus, the average power will be minimized if

the coordinate system wherein power is calculated has its origin

at the center of mass of the signal points.

In the investigation reported in the body of this paper, it

has been assumed that all signals (code words) are equally likely,

i.e., Pi = 1/M. By construction, each signal is in one-one corre-

spondence with a word of a group code (linear code) and the ensemble

of M signals has constituted a complete group code. It will now be

shown that, under these constraints, the origin of coordinates

(0, 0, ' 0) of these signals is at the center of mass of the

signal points and hence minimum average power is required.

If the center of mass of the system of signal points is to

coincide with the origin of coordinates, then R = 0, or, from (4)
M
r = pix = 0
i=l

for all j. By hypothesis, pi = 1/M, requiring

M
S x =0,
iMij
i=l










or
M
x = 0 (7)
ij
i=l1


Since x can assume only the values + B, then (7) will be satisfied
ij
if and only if the positive and negative values each appear M/2

times in the jth component of the signal.

We will use the notation of linear codes. Consider an (n,k)

linear code over the field of two elements. It is such a code which

has been used in forming the signal vectors, by mapping 1 into B and

0 into -B. Hence, any property derived for the (n,k) code will be

valid for the signals used in this investigation.

This (n,k) linear code contains M = 2k n-tuples over the

field of two elements, and these n-tuples form a subspace V of the

vector space of all n-tuples over the field of two elements. Arrange

these n-tuples as rows of a matrix. No column of this matrix contains

all zeros, for if such a column existed, it could be deleted and an

(n-l,k) linear code would remain.

Consider the subset of vectors of V, v1, v2, * = S,

which have a 0 in the jt column. Now S is a subspace of V since S

is closed under addition (the sum of any two vectors with 0 in the

jth column will be a vector with 0 in the jth column) and closed under

multiplication by scalar field elements (any vector which has a zero

in the jth component will retain a zero in that component when multi-

plied by any scalar.) Since S is a subspace of a vector space V,

S is also a vector space and is an Abelian group under addition.










Hence, we may form left costs based upon S as a subgroup. Selecting

a member of V which contains a 1 in the jth position, we can form a

left coset. Now, all members of V which contain a 1 in the jth

position must appear in this coset, since two elements v and v' of a

group V are in the same left coset of a subgroup S of V if and only

if (-v) + v' is an element of S, and the sum of any two vectors with

a 1 in the jth position yields a vector with a 0 in the jth position.

This theorem is proved by Peterson (21). Since the vectors of V can

have only a 1 or 0 in the jth position, this partitioning exhausts V

and divides the space into a subset containing all O's in the jth

position and another subset containing all l's in the jth position. By

construction, each subset has an identical number of elements. Thus,

each must contain M/2 vectors. Consequently, in a linear code, l's

and O's occur M/2 times in each component of the vector ensemble. Thus,

if a linear code is mapped into a code containing components of + B

in each vector, (7) is satisfied. Hence, minimum power is required.


B. An Upper Bound on Reliability for the Binary Symmetric Channel


It was felt that the technique used in the derivation of the

first upper bound in Section III, Part B, of the body of this paper,

might yield a comparable result for binary codes used in the binary

symmetric channel (BSC). The resulting bound does prove to be lower

than the sphere-packing bound for low transmission rates.

The Plotkin bound on maximum minimum distance guarantees that,

for an (n,k) code, there exists at least one pair of words, wl and w2,










which are no farther apart than the bound value of d, which is given

by

d S E 1 R + 1 log (n k + 1) + 1 (1)
2 L n n

where R = k/n = rate in bits, and logs are to the base 2. Assume

that, in a BSC, one of this pair which has maximum minimum distance,

say wl, is transmitted. Then the contribution to the probability

of error of the code is certainly no less than the probability that

w, is mistaken for w2, weighted by the probability that wl is trans-

mitted. Since all words are equally likely, the probability
-k
that wl is transmitted is 2-k. The word w1 will be mistaken for w2

if at least d/2 of the d places in which they differ are changed.

If the BSC has an error probability of p and a probability of correct

transmission of q = (1 p), then the contribution to the probability

of error of the code, P is bounded by

d

P T -k p i q i (2)
re '
i=d
2

Now, discard w since an expression bounding its contribution to the
k
P has been obtained. The remaining (2 1) words must contain a
e

pair which is no farther apart than the bound value of d,, which is

given by


d S [ i 1 log (2 1) + 1 log (n k + 1) + (3)
1 2 n n n










Similarly, if one of this pair is transmitted and decoded as the

other, it will yield a contribution to the probability of error

of at least

d(d) qdl 1
d 2-k 1 P i

i=d
2

This can be continued until only two words remain. This last con-

tribution will be


2-k do) pi d -


d
2o
2do
2


where


d 5 1 --+1 log (n
o 2 [ n n

If the contributions from

results a lower bound on P given
e


S2k- 2

2


i
p q


- k + 1) + 2-]n


all these pairs are summed, there

by


d i + d dl pi q di
i dl i
S-
2


d d p d (4)
+ + od P (4)

i= i
2

This bound can be simplified. First, since there are (2k 1)

terms on the right of (4), the inequality is preserved if we retain

only the first (2k + 1) terms. Each term on the right of (4)

can be shown to be smaller than the preceding term. Hence, these










(2k 1 + 1) remaining terms can be replaced by the (2k 1+ 1)th

term, giving


k 1
2 e+
P _
e 12k


d

S()pi qd
i.d
2


where now d is given by the right side of the relation


d n2 I .log 2k 1
2 1 n


+ log (n k + 1) +2
n n


d SE 1 R + 1 log (n k + 1) + ]
21 n nj


The inequality (5) is preserved and somewhat simplified if it

is written


d
p a1 E d
e 2 i

2

Reliability E is, from (7)


i d i
p q


E S lim -n log[
n -+oo L2


d

di
2


d
E S lim -1 + log Z
n-oo n in


d d i d i

i




d ( pi d i
P q
d i
2


The evaluation of (8) requires that an estimate be made of










the "tail" of a binomial distribution. Peterson (22) gives the

necessary relations. Write d/2 as X d, where = 1/2. The sum in

the inequality (8) may then be written


d


i= Ad


lP q


and the inequality


d d i
i= Ad


-Xd /d
X


Xd /pd
P q


can be used, provided

length n increases, d

for large d,



log [
d L


, : p. Here ( X + ,) = 1. As the code word

will also increase. Peterson (22) shows that,


Xd -/ d Xd /d ,
fA. p q I = F( ), p)


where


F(A, p) = H(p) H(A) + ( A p) H'(p),

H(x) = -x log x (1 x) log (1 x),

and

H'(x) log (-x)


Thus, using (10), the bound (8) is written


E tli m I
n-utoo Plotkin

But, from the Plotkin


+ d F( p)
n J


bound (6),


(10)


(11)









d [l R +l log (n k + 1) + 1 .
n 2 n nJ

Also, recalling that X = 1/2, there results, in the limit,


E E F(0.5, p) (1 R) .(12)
2


The optimum or sphere-packing bound for the binary symme-

tric channel is due to Elias (23). It is given by

E S F()o, p) (13)


where F(0 p) is given by (11), and \ is defined by the
O o
expression

S- H(X ) = R (14)
o

Figure 13 shows a typical plot of the bound derived here, as given

by (12), and the sphere-packing bound as given by (13), for

p = 0.01. The bound (13) is lower than the sphere-packing bound for

low transmission rates R.

Since the Plotkin bound (1) is applicable to binary codes in

general, the results obtained here for Pe, expression (7), and E,

expression (12), are valid for the entire class of binary codes.

Weldon (24) has obtained low rate results for the BSC which are

applicable to group codes only. His result for E is the same as

(12), while his result for P is higher than (7) by a factor of 2.















1.2






1.0






0.8






0.6


Sphere-Packing
Bound


0 0.2 0.4 0.6 0.8


Figure 13.


Rate R

Low Rate Bound Compared
Bound for p = 0.01


with Sphere-Packing


Low Rate Bound


0.4





0.2













LIST OF REFERENCES


1. C. E. Shannon and W. Weaver, "The Mathematical Theory of Com-
munication," University of Illinois Press, Urbana, Ill.; 1949.

2 W. W. Peterson, "Error-Correcting Codes," John Wiley and Sons,
Inc., New York, N. Y.; 1960.

3. P. Elias, "Error-Free Coding," IRE Transactions on Information
Theory, vol. IT-4, pp. 29-37; 1954.

4. C. E. Shannon, "Probability of Error for Optimal Codes in a
Gaussian Channel," Bell System Technical Journal, vol. 38,
pp. 611-656; May, 1959.

5. G. A. Franco and G. Lachs, "An Orthogonal Coding Technique for
Communication," IRE Convention Record, part 8, pp. 126-133;
March, 1961.

6. F. F. Harmuth, "Orthogonal Codes," Institution of Electrical
Engineers, monograph 369E; March, 1960.

7. R. M. Fano, "Transmission of Information," John Wiley and Sons,
Inc., New York, N. Y., pp. 148-163; 1961.

8. R. M. Fano, op, cit., p. 141.

9. D. M. Y. Sommerville, "An Introduction to the Geometry of
N Dimensions," Dover Publications, Inc., New York, N. Y,,
p. 76; 1958.

10. H. Cramer, "Mathematical Methods of Statistics," Princeton
University Press, Princeton, N. J., pp. 310-312; 1946.

11. M. Plotkin, "Binary Codes with Specified Minimum Distance,"
IRE Transactions on Information Theory, vol. IT-6, pp, 445-450;
September, 1960.

12. W. W, Peterson, op. cit., pp. 48-50.

13. F. M. Reza, "An Introduction to Information Theory," McGraw-
Hill Book Company, Inc., New York, N. Y., pp. 83-84; 1961.










14. W. Feller, "An Introduction to Probability Theory and Its
Applications," John Wiley and Sons, Inc., New York, N. Y.,
p. 131; 1950.

15. E. N Gilbert, "A Comparison of Signaling Alphabets," Bell
System Technical Journal, vol. 31, pp. 504-522; 1952.

16. W. W. Peterson, op. cit., pp. 51-52

17. R, W. Hamming, "Error Detecting and Error Correcting Codes,"
Bell System Technical Journal, vol. 29, pp. 147-160; 1960.

18. W. W, Peterson, op. cit., pp. 52-54.

19. R. M. Fano, op. cit., pp. 224-231.

20. R. S. Kennedy and J. M. Wozencraft, "Coding and Communication,"
Massachusetts Institute of Technology, Cambridge, Mass., MIT
Report No. MS-927; 1963.

21. W. W. Peterson, op. cit., p. 17,

22. W. W. Peterson, op. cit., pp. 246-247.

23. P. Elias, "Coding for Two Noisy Channels," Third London
Symposium on Information Theory, Academic Press, Inc., New
York, N. Y.; 1956.

24. E. J. Weldon, "Asymptotic Error Coding Bounds for the Binary
Symmetric Channel with Feedback," Air Force Cambridge Research
Laboratories, Bedford, Mass., AFCRL Report No. 63-122; April,
1963.













BIOGRAPHICAL SKETCH


James Robert Wood was born February 14, 1931, in Memphis,

Tennessee. In June, 1948, he was graduated from Bay County High

School, Panama City, Florida. He received the degree of Bachelor

of Electrical Engineering in August, 1956, from the University of

Florida. His undergraduate studies were interrupted from 1951 to

1955, when he served four years with the United States Air Force,

chiefly in Japan. In August, 1956, he joined the International

Business Machines Corporation, with whom he has been associated

until the present time. He has held several positions with that

company and had assignments in various phases of digital computer

development. He attended graduate school in the extension division

of Syracuse University during the years 1957 to 1961 and received

the degree Master of Electrical Engineering from that institution

in August, 1961. In 1961 he was awarded an IBM fellowship and began

work toward the degree of Doctor of Philosophy in September, 1961.

James Robert Wood is married to the former Barbara Lois

Braswell and has one son. He is a member of the Institute of

Electrical and Electronic Engineers, Phi Kappa Phi, and Sigma Tau.









This dissertation was prepared under the direction of the

chairman of the candidate's supervisory committee and has been

approved by all members of that committee. It was submitted to the

Dean of the College of Engineering and to the Graduate Council,

and was approved as partial fulfillment of the requirements for

the degree of Doctor of Philosophy.



August 8, 1964


Dean, College of Engineering


Dean, Graduate School


Supervisory Committee


Chairman


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