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IEEE International Symposium on Information Theory (ISIT)

Analysis and design of ultra-sparse non-binary cluster-LDPC codes

David Declercq, Valentin Savin, Lam Pham Sy

Published Jul 1, 2012• 3 citations
Publisher

Abstract

This paper continues a previous work on non-binary cluster-LDPC codes. Such codes are defined by locally dense parity-check matrices, with (possibly rectangular) dense clusters of bits, but which are cluster-wise sparse. We derive a lower bound on the minimum distance of non-binary cluster-LDPC codes that is based on the topological properties of the underlying bipartite graph. We also propose an optimization procedure, which allows designing finite length codes with large minimum distance, even in the extreme case of codes defined by ultra-sparse graphs, i.e. graphs with all symbol-nodes of degree dv= 2. Furthermore, we provide asymptotic thresholds of ensembles of non-binary cluster-LDPC codes, which are computed exactly under the Belief Propagation decoding, and upper-bounded under the Maximum a Posteriori (MAP) decoding. We show that the MAP-threshold upper bounds, which are conjunctured to be tight, quickly approach the channel capacity, which confirms the excellent minimal distance properties of non-binary cluster-LDPC codes.

DOI
10.1109/isit.2012.6283973

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In this paper, we study the asymptotic minimum distance of non-binary cluster-LDPC codes whose subjacent binary parity-check matrix is composed of localized density of ones, concentrated in clusters of bits. A particular attention is given to cluster codes represented by ultra-sparse bipartite graphs, in the sense that each symbol-node is connected to exactly dv= 2 constraint-nodes. We derive a lower bound on the minimum distance of non-binary cluster-LDPC codes and we show that there exist ensembles of ultra-sparse codes whose minimum distance grows linearly with the code length (with probability going to 1 as the code length goes to infinity). This result is in contrast with “classical” non-binary LDPC codes based on graphs with strictly regular dv= 2 symbol-nodes, whose minimum distance grows at most logarithmically with the code length. We also show that one can build practical non-binary cluster-LDPC codes with various finite codeword lengths, whose minimum distance is close to the Gilbert-Varshamov bound.

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Weight Distribution for Non-binary Cluster LDPC Code Ensemble

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This paper derives the average symbol and bit weight distributions for the irregular non-binary cluster low-density parity-check (LDPC) code ensembles. Moreover, we give the exponential growth rates of the average weight distributions in the limit of large code length. We show the condition that the typical minimum distances linearly grow with the code length.

arXiv:1305.2480•IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences•Jan 1, 2013•2 citations
This paper continues a previous work on non-binary cluster-LDPC codes. Such codes are defined by locally dense parity-check matrices, with (possibly rectangular) dense clusters of bits, but which are cluster-wise sparse. We derive a lower bound on the minimum distance of non-binary cluster-LDPC codes that is based on the topological properties of the underlying bipartite graph. We also propose an optimization procedure, which allows designing finite length codes with large minimum distance, even in the extreme case of codes defined by ultra-sparse graphs, i.e. graphs with all symbol-nodes of degree dv= 2. Furthermore, we provide asymptotic thresholds of ensembles of non-binary cluster-LDPC codes, which are computed exactly under the Belief Propagation decoding, and upper-bounded under the Maximum a Posteriori (MAP) decoding. We show that the MAP-threshold upper bounds, which are conjunctured to be tight, quickly approach the channel capacity, which confirms the excellent minimal distance properties of non-binary cluster-LDPC codes.
In this paper, we study the asymptotic minimum distance of non-binary cluster-LDPC codes whose subjacent binary parity-check matrix is composed of localized density of ones, concentrated in clusters of bits. A particular attention is given to cluster codes represented by ultra-sparse bipartite graphs, in the sense that each symbol-node is connected to exactly dv= 2 constraint-nodes. We derive a lower bound on the minimum distance of non-binary cluster-LDPC codes and we show that there exist ensembles of ultra-sparse codes whose minimum distance grows linearly with the code length (with probability going to 1 as the code length goes to infinity). This result is in contrast with “classical” non-binary LDPC codes based on graphs with strictly regular dv= 2 symbol-nodes, whose minimum distance grows at most logarithmically with the code length. We also show that one can build practical non-binary cluster-LDPC codes with various finite codeword lengths, whose minimum distance is close to the Gilbert-Varshamov bound.
The performance of maximum-likelihood (ML) decoding on the binary erasure channel for finite-length low-density parity-check (LDPC) codes from two random ensembles is studied. A tightened union-type upper bound on the ML decoding error probability based on the precise coefficients of the average weight spectrum is presented. For LDPC codes from the Gallager ensemble and the Richardson-Urbanke ensemble, new upper bounds on the ML decoding performance based on computing the rank of submatrices of the code parity-check matrix are derived. A new lower bound on the ML decoding threshold followed from the latter error probability bound is obtained. An improved lower bound on the error probability for codes with a known estimate on the minimum distance is presented as well. A new low-complexity near-ML decoding algorithm for quasi-cyclic LDPC codes is proposed and simulated. Its performance is compared to the simulated belief propagation and ML decoding performance and simulated performance of the best known improved iterative decoding techniques, as well as, with the derived upper bounds on the ML decoding performance and with decoding thresholds obtained by the density evolution technique.
Low-density parity-check (LDPC) block codes invented by Gallager in 1962 achieve exceptional error performance on a wide variety of communication channels. LDPC convolutional codes are the convolutional counterparts of LDPC block codes. This dissertation describes techniques for the design of LDPC convolutional codes, and analyzes their distance properties and iterative decoding convergence thresholds. The construction of time invariant LDPC convolutional codes by unwrapping the Tanner graph of algebraically constructed quasi-cyclic LDPC codes is described. The convolutional codes outperform the quasi-cyclic codes from which they are derived. The design of parity-check matrices for time invariant LDPC convolutional codes by the polynomial extension of a base matrix is proposed. An upper bound on free distance, proving that time invariant LDPC codes are not asymptotically good, is obtained. The Tanner graph is used to describe a pipelined message passing based iterative decoder for LDPC convolutional codes that outputs decoding results continuously. The performance of LDPC block and convolutional codes are compared for fixed decoding parameters like computational complexity, processor complexity, and decoding delay. In each case, the LDPC convolutional code performance is at least as good as that of LDPC block codes. An analog circuit to implement pipelined decoding of LDPC convolutional codes is proposed. The distance properties of a permutation matrix based (time varying) ensemble of (J,K) regular LDPC convolutional codes is investigated. It is proved that these codes (for J > 2) have free distance increasing linearly with constraint length, i.e., they are asymptotically good. Further, the asymptotic free distance to constraint length ratio for the convolutional codes is several times larger than the minimum distance to block length ratio for corresponding LDPC block codes. Iterative decoding of terminated LDPC convolutional codes, based on the ensemble mentioned above, is analyzed, assuming transmission over a binary erasure channel. The structured irregularity of the codes leads to significantly better convergence thresholds compared to corresponding LDPC block codes. At the calculated thresholds, both the bit and block error probability can be made arbitrarily small. The results obtained suggest that the thresholds approach capacity with increasing J.
In the first part of this thesis we are interested in the asymptotic performance analysis of Non-Binary Low-Density Parity-Check (NBLDPC) codes over the Binary Erasure Channel (BEC) decoded via the suboptimal Belief Propagation (BP) decoder as well as the optimal Maximum-A-Posteriori (MAP) decoder. Previously, NBLDPC codes defined with respect to a finite field have been studied in the literature. Unfortunately, for these codes the asymptotic analysis of the BP decoder in terms of density evolution is cumbersome since the involved densities "live" in a high dimensional space. To alleviate this problem, we introduce ensembles defined with respect to the general linear group. For these ensembles the density evolution equations can be written in a compact form. We compute thresholds for different alphabet sizes for various NBLDPC ensembles. Surprisingly, the threshold is not a monotonic function of the alphabet size. We also conjecture the stability condition for NBLDPC ensembles defined with respect to finite fields over any binary memoryless symmetric channel. We then consider the performance of NBLDPC codes under MAP decoding when transmission takes place over the BEC. Towards this goal, we generalize the concepts of the peeling decoder and stopping sets to NBLDPC codes. Using these concepts, we give a combinatorial characterization of decoding failures for NBLDPC codes decoded via the BP decoder. We use the decoding failure criterion and the density evolution analysis to compute the asymptotic residual degree distribution for NBLDPC codes. The rate of this residual ensemble gives the conditional entropy of the NBLDPC code. To compute the rate of the residual ensemble we generalize the criterion of [1] to the non-binary setting, which, when satisfied, guarantees that almost every code in the ensemble has a rate equal to the design rate. We observe that the Maxwell construction of [1], relating the performance of MAP and BP decoding via the Extended BP GEXIT (EBP GEXIT [2]) function, holds in the setting of NBLDPC codes. The weight and stopping set distribution of a code are related to its performance under the MAP and the BP decoder respectively. We first concentrate on the weight distribution of regular NBLDPC ensembles. We derive the average weight distribution of regular NBLDPC ensembles. Using this, we derive the average of equivalent binary weight distribution of regular NBLDPC ensembles. We show that as the alphabet size becomes larger the equivalent binary weight distribution converges to the weight distribution of Gallager's random parity check ensemble. The average weight distribution of binary LDPC ensembles has been extensively studied in the literature. Much less is known about the probability distribution of this quantity. In particular, the question of the concentration of the weight distribution around the ensemble average has not been addressed. We compute the variance of the weight distribution of binary regular LDPC ensembles. Using this and the second moment method we obtain bounds on the probability that a randomly chosen code has its weight distribution close to the ensemble average. We show that a large fraction of the total number of codes have their weight distribution close to the average. We apply the same technique for the stopping set distribution of binary regular LDPC ensembles. Again we show that a large fraction of total number of codes have their stopping set distribution close to the ensemble average. In the last part of this thesis we address the question of the existence of the EBP GEXIT function. The EBP GEXIT function plays a fundamental role in the asymptotic analysis of sparse graph codes. For transmission over the BEC using binary LDPC codes, the analytic properties of the EBP GEXIT function are relatively simple and well understood. The case of general BMS channel is much harder and even the existence of the curve is not known in general. We introduce some tools from non-linear analysis which can be useful to prove the existence of EXIT like curves in some cases. The main tool is the Krasnoselskii-Rabinowitz (KR) bifurcation theorem.