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2004 Justesen.

INFORMATION THEORY AND INFORMATION PROCESSING Some Results and some Problems Related to Concatenated Codes J. Justesen COM, Technical University of Denmark, Lyngby, Denmark Received November 22, 2004 AbstractSome results of the cooperation between IPPI and the coding group in Denmark are reviewed. From this starting point several new developments and open problems in concatenated codes are discussed.

1. INTRODUCTION In this presentation, I mention some classical results on concatenated codes, and in particular I touch upon some results of the long cooperation between the laboratory of Victor Zyablov at IPPI and the coding group at DTU. However, the aim is not to give an account of the history of the topic, but to use these results as a starting point for discussing some recent developments and some open problems.

Concatenated codes have been at the focus of the research in both groups for more than three decades.

The reason is that not only has this technique become increasingly important in applications, but it has also maintained its position as an essential method in theoretical analysis, and it has absorbed several new developments in coding theory.

2. REED-SOLOMON CODES A discussion of concatenated codes necessarily involves some details of Reed-Solomon codes as an essential component of these constructions. Reed-Solomon codes were some of the first codes to appear in the literature, and I prefer to take them as the starting point for developing algebraic coding theory. Their properties can be explained in very simple terms:

Let the data be a sequence of K symbols, interpreted as coefficients of a polynomial U(x). The transmitted codeword is then a sequence of N > K values attained by this polynomial in N district points:

c = [U(x0), U(x1),..., U(xN-1)] (1) Two distinct codewords can agree in at most K - 1 points, since the difference polynomial can have at most K - 1 roots.

Assume that T of the received values are in error. Decoding may be seen as an interpolation; the receiver should find a polynomial which passes through as many of the received points as possible. The positions of the errors are found by determining the coefficients of a polynomial E(x) of degree T with the error positions as roots. If the received values are yj, we have M(x) = U(x)E(x) M(xj) = yjE(xj) (2) since either U(xj) = yj PROBLEMS OF CONCATENATED CODES or both sides are zero. If T (N - K)/2, there are enough linear equations to find the coefficients of M and E.

The description of the coding problem in such basic terms has allowed new results in fundamental computer science to be applied in our area.

3. CONCATENATED CODES In Forneys thesis [1], it was proved that concatenated codes using a combination of RS (outer) codes and binary codes for each RS symbol (inner codes) could be used to reach the channel capacity with a favorable combination of error probability and computational complexity. However, in reality this goal appeared out of reach at the time.

Several results in the early 70s changed this perspective. A specific combination of inner and outer codes was suggested as a practical way of obtaining good performance on deep-space channels. The space telemetry standard is specified in detail in the Blue Book of the Consultative Committee for Space Data Systems [2], an organization that has the American NASA, the European ESA, and the Russian Space Agency among its members.

While in [1] concatenation was seen almost as a way of avoiding the construction of long codes, it was found that the process could also be seen as a way of constructing a single long code with fairly good parameters [3, 4]. Here a codeword is seen as a binary array where column j is a binary codeword in the inner code representing the symbol cj = U(xj). Thus a solution was found to the problem of constructing long codes with good distances. This point of view led to algorithms that correct all errors within half the designed minimum distance. It follows immediately from the two-level construction that the distance of the concatenated code is at least the product of the distances of the component codes. This bound is often referred to as the Zyablov bound.

Unfortunately the results on the performance on the Binary Symmetric Channel (or the Gaussian Channel) and the results on the distance properties are not clearly related. The exact distance of the standard code is not known, and probably of little importance in applications. On the other hand algorithms that guarantee correction of a certain number of errors are usually not effective with a random distribution of errors.

The separation between the results related to performance and to distances is further amplified by the use of convolutional inner codes in most applications. There has been relatively little research in the properties of concatenated codes with convolutional inner codes, although we established some of the fundamental properties in [5]. Recently there has been some interest in so-called tail-biting codes as a bridge between convolutional codes and block codes [6]. In a tail-biting code the encoding is done by a linear system as in convolutional codes, but the encoding process circles through a limited number of information bits to produce a block code. This approach would make the decoding of each column of symbols in a concatenated code independent of the rest of the code, providing some benefits both for the implementation and the analysis. With a suitable choice of parameters the performance would not change much, but it is unknown whether it can actually be improved by the right choice of parameters.

By considering averages of codes with random inner codes, it has been proved that concatenated codes can have the same properties as general average codes with the same parameters [7, 8]. However, in this case the parameters of the component codes have to be chosen in a way that appears to rule out separate decoding of the inner and outer codes. There are several known cases where the binary images of RS codes are very good binary codes. A recently reported example is the (160, 80, 24) codes [9]. For low-rate concatenated codes with good short inner codes, the Zyablov bound is sufficient to prove that the binary codes have good distances. Codes of length 85 can serve as a small example, but no specific good code with a non-trivial inner code and distance above the Zyablov bound has been reported.

4 3 286 JUSTESEN 4. APPLICATIONS IN COMMUNICATION SYSTEMS In 1985/86 the concatenated coding standard was used in two spectacular space missions. In NASAs Voyager the code was used to send back images and telemetry from the planet Uranus, and Voyager has since continued to transmit data from the outer parts of the Solar system. ESAs GIOTTO probe made a close encounter with Halleys comet producing closeup images and measurements of the environment of the active comet. In this case the communication problem was not related to the distance, but to the unknown hazards of the encounter. Actually the probe survived the flyby, but the signal level started varying periodically when dust particles made the probe oscillate around its proper alignment.

Our work on the GIOTTO decoder produced an important feedback from the realities of communication systems. In particular it demonstrated that the interaction of the error-correcting code with other parts of the communication system is more complicated than we expected, and that building the decoders was a less complicated task. The activities since then have further emphasized these points.

In addition to a growing number of applications in communication systems, concatenated codes are now also commonly used in digital storage media [10].

A long concatenated code is, at least in this context, part of the structure of a block of data commonly referred to as a frame. In addition to the user data, the frame contains a synchronization pattern and some header information like a serial number and an address. The decoding of the RS code requires that the beginning of the frame is correctly identified, and even in the presence of errors this decision can usually be made with sufficient reliability. But the interaction of error-correction and synchronization is not adequately described in the literature. The header information must usually be available without complete decoding of the frame. In storage systems that can lead to problems with false sequence numbers, whereas in networks the addresses are a particular concern. This is an area that has frequently been marred by design errors, and network models in particular do not have a consistent way of treating error-correcting codes.

The implementation of decoders for concatenated codes shows that RS codes are well suited for modern digital technology in spite of their apparent complexity. At low data rates, a suitable program for a microprocessor may be the choice, but usually faster solutions are required. At present decoders are often implemented in Field Programmable Gate Arrays (FPGA). In this way the circuit is actually configured by downloading a program, and it is even possible to make changes later by modifying the software. Standard decoding methods (syndrome calculation, solution for the error locator polynomial by Euclids algorithm, etc.) can be efficiently converted to parallel versions and used in such systems. Such algorithms are still commonly used, but there is a long line of research in faster methods based on fast transforms, probably starting with Afanasyevs paper [11]. The fast algorithms allow a significant speed-up of parallel decoders, and the use of RS codes in optical communication systems may eventually make such decoders a reality.

However, while there is some work done on the details of the implementations in the industry [12, 13], the theoretical work on complexity does not seem to have produced results on space/time bounds for parallel algorithms.

5. ALGEBRAIC GEOMETRY CODES AS OUTER CODES One of the spectacular advances in coding theory in the 80s was the development of long codes over large alphabets using methods from Algebraic Geometry. Such codes are generalizations of RS codes in the sense that codewords are obtained by evaluating an information polynomial as in (1), but xj = (vj, wj) is a point of an algebraic curve, usually a point in the plane satisfying some defining equation F (v, w) = In this way it is possible to keep the alphabet fixed and let the length of the code increase. Decoding of such codes can be seen as a generalization of decoding RS codes as described in [14], which is in part based 4 3 PROBLEMS OF CONCATENATED CODES on work I did while visiting IPPI. However, a version of the algorithm that exploits fast transforms has not yet been described in a satisfying form.

While algebraic geometry so far has not produced good direct constructions of binary codes, long binary codes have been obtained by concatenation [15]. We can also fix the inner code in a concatenated code and consider outer codes of increasing length with a randomized mapping of the symbols [16].

6. DECODING MORE ERRORS If the decoding of the inner code leads to more than (N - K)/2 errors, the result of decoding the RS code is usually a decoding failure. Even though it is easy to construct examples where additional errors cause decoding to a wrong codeword, such cases are extremely rare, and in principle more errors can be corrected.

Efforts to modify the decoding methods had very limited success until Sudans work on interpolation was applied to the problem [17]. Sudans idea was to rewrite (2) as Q(xj, zj) = yj and factor the two-variable interpolating polynomial Q. In this way one will in general find a list of factors of the form z - Ui(x) which represent different possible codewords. Usually there is only one factor, and the major improvement is that, at least for low rates, some additional errors can be corrected.

For concatenated codes, more efficient decoding would be possible if the inner decoder produced a short list of possible codewords rather than a single result. This approach was first discussed in Pinsker and Zyablovs paper [7], where the authors proved that decoding of the inner code within a suitable sphere would always produce a very small number of results. In [7] this result was combined with a complex approach to the decoding of the outer codes. Recently there has been some work on using a list input in a generalized version of Sudans algorithm [18]. However, the gains for concatenated codes so far appear to be limited.

It may be useful to note that if the input list is very small and there are no errors, the RS code can be decoded by solving a system of linear equations: Let the symbols in some subsets of the positions j J have two possible values. Choose one of the values arbitrarily, and calculate the syndrome of the received word, s. For each position find the change that the other choice of the symbol would add to the syndrome, and solve the binary equations ajsj jJ where aj = 1 indicates that the symbol in position j should be changed. However, no way of combining this approach with error correction is known.

A more recent variant of the interpolation approach allows more errors to be corrected when several codewords are interleaved (as it is common usage), and the errors in all codewords are confined to a common set of positions [19]. Thus (2) becomes Mi(x) = Ui(x)E(x) Mi(xj) = yijE(xj) and by comparing the number of equations and the number of unknown coefficients one finds that as many as (N - K)I/(I + 1) 4 3 288 JUSTESEN errors can be corrected where I is the number of outer codes. Clearly this is not always possible. However, as indicated in [20] there is actually a high probability of successful decoding. The method appears very well suited for decoding of concatenated codes, but more work is needed to establish the choice of parameters and details of the decoding algorithm that will give a real gain.

7. CONCATENATED CODES FROM GRAPHS Recently a generalized form of concatenated codes has been described as codes defined on bipartite expander graphs [21]. A special case is studied in more details in [22], and here the connection to the usual concatenated codes is also made more explicit.

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