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IEEE Transactions on Information Theory

On decoding linear error-correcting codes--I

Neal Zierler

Published Sep 1, 1960• 12 citations
Publisher

Abstract

A technique is described for finding simply computable numerical-valued functions of a received binary word whose value indicates where errors in transmission have occurred. Although it seems that a certain condition must usually be fulfilled for such functions to exist, or for our method to constitute an efficient procedure for finding them, there is, on the one hand, a strong tendency for "good" codes to satisfy the condition, while, on the other, it appears to be straightforward to construct codes which are good for a specified channel and also fulfill the condition. An advantage of the resulting decoding procedure is that it corrects and detects all possible errors; more precisely, if a worduis received and the coset\bar{u}to whichubelongs has a unique leadere, the procedure concludes thatu + ewas sent, while ifuhas no unique leader, that fact, along with the weight of\bar{u}(and sometimes a little more) can be indicated. The ideas and techniques are illustrated by the construction of decoding procedures for the perfect (23, 12) three-error-correcting code.

DOI
10.1109/tit.1960.1057585

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Introduction Error-correcting codes were introduced in the 1940s in order to implement a theorem of Shannon's [260] which guaranteed that virtually error-free communication could be obtained even over a noisy channel. The message to be communicated is first “encoded”, i.e. turned into a codeword, by adding "redundancy". The codeword is then sent through the channel and the received message is “decoded” by the receiver into a message resembling, as closely as possible, the original message. The degree of resemblance will depend on how good the code is in relation to the channel. The usual pictorial representation of this communication link is shown in the schematic diagram Figure 2.1. Here a message is first given by the source to the encoder that turns the message into a codeword, i.e. a string of letters from some alphabet, chosen according to the code used; the encoded message is then sent through the channel, where it may be subjected to noise and hence altered; this possibly-altered message, when it arrives at the decoder belonging to the receiver, is, first of all, equated with the most likely codeword, i.e. the one (should that exist) that, in a probabilistic sense depending on the channel, was probably sent; finally this “most likely” codeword is decoded and the message is passed on to the receiver. Example 2.1.1 Suppose we use an alphabet of just two symbols, 0 and 1, and we have only two messages, for example “no” corresponding to 0, and "yes” corresponding to 1. We wish to send the message “no”, and we add redundancy by simply repeating the message five times. Thus we encode the message as the codeword (00000). The channel interferes, perhaps, with the message and could, for example, change it to (01100). The decoder assesses the message and decides that of the two possible codewords, i.e. (00000) and (11111), the former is the more likely, and hence the message is decoded, correctly, as “no”. Notice that we have made several assumptions here: for example we have assumed that the probability of an error at any position in the word is less than, that each codeword is equally likely to be sent, and that the receiver is aware of the code used.

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•Jan 1, 1987•0 citations
A technique is described for finding simply computable numerical-valued functions of a received binary word whose value indicates where errors in transmission have occurred. Although it seems that a certain condition must usually be fulfilled for such functions to exist, or for our method to constitute an efficient procedure for finding them, there is, on the one hand, a strong tendency for "good" codes to satisfy the condition, while, on the other, it appears to be straightforward to construct codes which are good for a specified channel and also fulfill the condition. An advantage of the resulting decoding procedure is that it corrects and detects all possible errors; more precisely, if a worduis received and the coset\bar{u}to whichubelongs has a unique leadere, the procedure concludes thatu + ewas sent, while ifuhas no unique leader, that fact, along with the weight of\bar{u}(and sometimes a little more) can be indicated. The ideas and techniques are illustrated by the construction of decoding procedures for the perfect (23, 12) three-error-correcting code.
Introduction Error-correcting codes were introduced in the 1940s in order to implement a theorem of Shannon's [260] which guaranteed that virtually error-free communication could be obtained even over a noisy channel. The message to be communicated is first “encoded”, i.e. turned into a codeword, by adding "redundancy". The codeword is then sent through the channel and the received message is “decoded” by the receiver into a message resembling, as closely as possible, the original message. The degree of resemblance will depend on how good the code is in relation to the channel. The usual pictorial representation of this communication link is shown in the schematic diagram Figure 2.1. Here a message is first given by the source to the encoder that turns the message into a codeword, i.e. a string of letters from some alphabet, chosen according to the code used; the encoded message is then sent through the channel, where it may be subjected to noise and hence altered; this possibly-altered message, when it arrives at the decoder belonging to the receiver, is, first of all, equated with the most likely codeword, i.e. the one (should that exist) that, in a probabilistic sense depending on the channel, was probably sent; finally this “most likely” codeword is decoded and the message is passed on to the receiver. Example 2.1.1 Suppose we use an alphabet of just two symbols, 0 and 1, and we have only two messages, for example “no” corresponding to 0, and "yes” corresponding to 1. We wish to send the message “no”, and we add redundancy by simply repeating the message five times. Thus we encode the message as the codeword (00000). The channel interferes, perhaps, with the message and could, for example, change it to (01100). The decoder assesses the message and decides that of the two possible codewords, i.e. (00000) and (11111), the former is the more likely, and hence the message is decoded, correctly, as “no”. Notice that we have made several assumptions here: for example we have assumed that the probability of an error at any position in the word is less than, that each codeword is equally likely to be sent, and that the receiver is aware of the code used.
We saw in Chapter 1 that the purpose of channel coding is to introduce redundancy to information messages so that errors that occur in the transmission can be detected or even corrected. In this chapter, we formalize and discuss the notions of error-detection and error-correction. We also introduce some well known decoding rules, i.e., methods that retrieve the original message sent by detecting and correcting the errors that have occurred in the transmission. Communication channels We begin with some basic definitions. Definition 2.1.1 Let A = { a 1 , a 2 , …, a q } be a set of size q , which we refer to as a code alphabet and whose elements are called code symbols . (i) A q-ary word of length n over A is a sequence w = w 1 w 2 … w n with each w i ∈ A for all i . Equivalently, w may also be regarded as the vector ( w 1 , …, w n ). (ii) A q-ary block code of length n over A is a nonempty set C of q -ary words having the same length n . (iii) An element of C is called a codeword in C . (iv) The number of codewords in C , denoted by ∣ C ∣, is called the size of C . (v) The (information) rate of a code C of length n is defined to be (log q ∣ C ∣)/ n . (vi) A code of length n and size M is called an (n, M)-code .
Abstract. Error-correcting codes are fundamental tools used to transmit digital infor-mation over unreliable channels. Their study goes back to the work of Hamming [Ham50] and Shannon [Sha48], who used them as the basis for the field of information theory. The problem of decoding the original information up to the full error-correcting potential of the system is often very complex, especially for modern codes that approach the theoretical limits of the communication channel. In this thesis we investigate the application of linear programming (LP) relaxation to the problem of decoding an error-correcting code. Linear programming relaxation is a standard technique in approximation algorithms and operations research, and is central to the study of efficient algorithms to find good (albeit suboptimal) solutions to very difficult optimization problems. Our new "LP decoders " have tight combinatorial characterizations of decoding success that can be used to analyze error-correcting performance. Furthermore, LP decoders have the desirable (and rare) property that whenever they output a result, it is guaranteed to be the optimal result: the most likely (ML) information sent over the channel. We refer to this property as the ML certificate property.
We shall not go into details concerning all the technical applications of error-correcting codes. These include satellite pictures, telephone messages via glass fibre using light, compact disc audio system. The idea is as follows. We consider “information” presented as a very long sequence of symbols from a finite set called the “alphabet”. In this course the alphabet will be a finite field IF q . In the sequence each symbol occurs with equal probability. This information is sent to a receiver over a so-called “noisy channel”. In the model that we consider there is a fixed (small) probability p e that a symbol, that is sent over the channel, is changed into one of the other symbols (again, all equally likely). Such an event is called a “symbol-error” and p e is the symbol-error probability. As a result a fraction p e of the transmitted symbols arrives incorrectly at the receiver end of the channel. The aim of coding theory is to lower the probability of error (considerably) at the expense of spending some of the transmission time or energy on redundant symbols. The idea is explained in one sentence as follows. When we read printed text we recognize a printing error in a word because in our vocabulary there is only one word that resembles (is “sufficiently close to”) the printed word.
This dissertation investigates the complexity of minimum-distance decoding (MDD) algorithms for general binary linear block codes, and also investigates the use of such codes in constructing error correcting/detecting codes that are effective against both random and unidirectional errors. A new MDD algorithm is proposed, and its complexity is analyzed and compared to that of other such algorithms. The question of whether all good codes satisfy certain properties required for efficient implementation of the algorithm is also considered. The performance of decoding algorithms, with the information bit error rate as the criterion instead of the block error rate, is also studied. New decoding rules which can yield lower information bit error rates than the MDD rule are proposed and evaluated. A new class of binary codes is developed which corrects random errors and detects all unidirectional errors. These codes are obtained by appending a suffix to each codeword of a linear block code. To evaluate the efficiency of the new codes, a thorough comparison with the best previously obtained codes is made.