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IEEE International Symposium on Information Theory (ISIT)

On the fundamental system of cycles in the bipartite graphs of LDPC code ensembles

Igal Sason

Published Jun 1, 2009• 1 citation
Publisher

Abstract

This work introduces an information-theoretic lower bound on the number of fundamental cycles for bipartite graphs of low-density parity-check (LDPC) code ensembles. This information-theoretic bound is expressed in terms of the achievable gap to capacity when the transmission of the code ensemble takes place over a memoryless binary-input output-symmetric (MBIOS) channel. The bound shows quantitatively the necessity of cycles in bipartite graphs which represent good LDPC code ensembles. More explicitly, it shows that the number of fundamental cycles should grow at least like log 1/epsiv where epsiv designates the gap in rate to capacity, hence, it is unbounded as the gap to capacity vanishes. For the derivation of this bound, a new information-theoretic lower bound on the average right degree, which also behaves like log 1/epsiv, is derived. The interested reader is referred to the full paper version.

DOI
10.1109/isit.2009.5205638

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This work introduces an information-theoretic lower bound on the number of fundamental cycles for bipartite graphs of low-density parity-check (LDPC) code ensembles. This information-theoretic bound is expressed in terms of the achievable gap to capacity when the transmission of the code ensemble takes place over a memoryless binary-input output-symmetric (MBIOS) channel. The bound shows quantitatively the necessity of cycles in bipartite graphs which represent good LDPC code ensembles. More explicitly, it shows that the number of fundamental cycles should grow at least like log 1/epsiv where epsiv designates the gap in rate to capacity, hence, it is unbounded as the gap to capacity vanishes. For the derivation of this bound, a new information-theoretic lower bound on the average right degree, which also behaves like log 1/epsiv, is derived. The interested reader is referred to the full paper version.
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We derive lower bounds on the density of parity-check matrices of binary linear codes which are used over memoryless binary-input output-symmetric (MBIOS) channels. The bounds are expressed in terms of the gap between the rate of these codes for which reliable communications is achievable and the channel capacity; they are valid for every sequence of binary linear block codes if there exists a decoding algorithm under which the average bit-error probability vanishes. For every MBIOS channel, we construct a sequence of ensembles of regular low-density parity-check (LDPC) codes, so that an upper bound on the asymptotic density of their parity-check matrices scales similarly to the lower bound. The tightness of the lower bound is demonstrated for the binary erasure channel by analyzing a sequence of ensembles of right-regular LDPC codes which was introduced by Shokrollahi, and which is known to achieve the capacity of this channel. Under iterative message-passing decoding, we show that this sequence of ensembles is asymptotically optimal (in a sense to be defined in this paper), strengthening a result of Shokrollahi. Finally, we derive lower bounds on the bit-error probability and on the gap to capacity for binary linear block codes which are represented by bipartite graphs, and study their performance limitations over MBIOS channels. The latter bounds provide a quantitative measure for the number of cycles of bipartite graphs which represent good error-correction codes.
This paper provides some universal information-theoretic bounds related to the degree distributions and the average cardinality of the fundamental system of cycles of low-density parity-check (LDPC) ensembles. The transmission of these ensembles is assumed to take place over an arbitrary memoryless binary-input output-symmetric (MBIOS) channel, and the bounds are expressed in terms of the gap between the design rates of these ensembles and the channel capacity. Some of these bounds hold under maximum-likelihood decoding (and hence, they also hold under any sub-optimal decoding algorithm) whereas the others hold particularly under the sum-product iterative decoding algorithm. The tightness of some of these bounds is exemplified numerically for capacity-approaching LDPC ensembles under iterative decoding; the bounds are reasonably tight for general MBIOS channels, and are tightened for the binary erasure channel where the tightness in the latter case is also demonstrated analytically.
We present lower bounds on the bit error probability of binary linear block codes, and on the gap between the channel capacity and the rate of these codes for which reliable communica- tion is achievable. The results are valid for memo- ryless binary-input output-symmetric (MBIOS) chan- nels. The lower bounds provide a quantitative mea- sure for the number of cycles of bipartite graphs which represent good error-correcting codes. The tightness of the lower bounds is especially pronounced for the binary erasure channel (BEC); to this end, we analyze a sequence of ensembles of low-density parity-check (LDPC) codes which closely approach these bounds. I. Introduction If a linear block code can be represented by a cycle-free fac- tor graph which only includes variable nodes and parity-check nodes, then maximum-likelihood soft decision decoding can be performed with low complexity (1). Theorem 5 in (1) indicates that these graphs represent codes with very poor minimum distance. The bounds in (1) only refer to the minimum dis- tance of cycle-free codes, and in this work we derive lower bounds on the bit error probability and the gap to capacity for codes on graphs which are represented by bipartite graphs (with or without cycles). We derive in this work information- theoretic lower bounds on the bit error probability of a binary linear code used over MBIOS channels. The bounds are ex- pressed in terms of the density of an arbitrary parity-check matrix which represents the binary linear code (i.e., the num- ber of ones in the matrix normalized per information bit), and they are valid for codes whose bipartite graphs are with or without cycles. We introduce a quantitative measure for the cycles in a bipartite graph which represents a binary lin- ear code (where the graph only includes variable nodes and parity-check nodes). The bounds provide an interpretation for the tradeofi which exists between the bit error proba- bility and gap to capacity of an arbitrary LDPC code (i.e., its performance limitations), and the density of an arbitrary parity-check matrix which represents the code (where the lat- ter afiects the decoding complexity per information bit and per iteration, under a message-passing iterative decoding al- gorithm). We present in this work quantitative results which indicate that in order to approach the channel capacity with vanishing bit error probability, LDPC codes should not have too sparse parity-check matrices, as otherwise their inherent gap to capacity becomes large. In fact, we show that the density of any parity-check matrix and the fundamental num- ber of cycles of the graph which represents the code should increase at least like log 1 where designates the gap to ca- pacity under iterative message-passing decoding (see (2), (3)). The tightness of the lower bounds which are derived in this work (and whose validity is for any sequence of codes with 1 1.5 2 2.5 3 3.5 4 4.5
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This paper considers the achievable rates and decoding complexity of low-density parity-check (LDPC) codes over statistically independent parallel channels. The paper starts with the derivation of bounds on the conditional entropy of the transmitted codeword given the received sequence at the output of the parallel channels; the component channels are considered to be memoryless, binary-input, and output-symmetric (MBIOS). These results serve for the derivation of an upper bound on the achievable rates of ensembles of LDPC codes under optimal maximum-likelihood (ML) decoding when their transmission takes place over parallel MBIOS channels. The paper relies on the latter bound for obtaining upper bounds on the achievable rates of ensembles of randomly and intentionally punctured LDPC codes over MBIOS channels. The paper also provides a lower bound on the decoding complexity (per iteration) of ensembles of LDPC codes under message-passing iterative decoding over parallel MBIOS channels; the bound is given in terms of the gap between the rate of these codes for which reliable communication is achievable and the channel capacity. The paper presents a diagram which shows interconnections between the theorems introduced in this paper and some other previously reported results. The setting which serves for the derivation of the bounds on the achievable rates and decoding complexity is general, and the bounds can be applied to other scenarios which can be treated as different forms of communication over parallel channels.