Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:0807.3065 · IEEE Transactions on Information Theory

Sharp Bounds for Optimal Decoding of Low-Density Parity-Check Codes

Shrinivas Kudekar, Nicolas Macris

Published Sep 16, 2009• 32 citations
arXivHTMLPDF

Abstract

Consider communication over a binary-input memoryless output-symmetric channel with low-density parity-check (LDPC) codes and maximumaposteriori(MAP) decoding. The replica method of spin glass theory allows to conjecture an analytic formula for the average input-output conditional entropy per bit in the infinite block length limit. Montanari proved a lower bound for this entropy, in the case of LDPC ensembles with convex check degree polynomial, which matches the replica formula. Here we extend this lower bound to any irregular LDPC ensemble. The new feature of our work is an analysis of the second derivative of the conditional input-output entropy with respect to noise. A close relation arises between this second derivative and correlation or mutual information of codebits. This allows us to extend the realm of the ldquointerpolation method,rdquo in particular, we show how channel symmetry allows to control the fluctuations of the ldquooverlap parametersrdquo.

DOI
10.1109/tit.2009.2027523

Related papers

Sharp Bounds for MAP Decoding of General Irregular LDPC Codes

Shrinivas Kudekar, Nicolas Macris

Consider communication over a binary input memoryless output symmetric channel with LDPC codes and MAP decoding. Recently Montanari proved that the replica solution is a lower bound to the conditional entropy for a class of LDPC ensembles. Here we extend this lower bound to any irregular LDPC ensemble for the BEC, BIAWGNC, BSC. Our work combines an analysis of the second derivative of the conditional entropy with respect to the noise and the interpolation method

IEEE International Symposium on Information Theory (ISIT)•Jul 1, 2006•14 citations

Tight Bounds for LDPC and LDGM Codes Under MAP Decoding

Andrea Montanari A

A new method for analyzing low-density parity-check (LDPC) codes and low-density generator-matrix (LDGM) codes under bit maximum a posteriori probability (MAP) decoding is introduced. The method is based on a rigorous approach to spin glasses developed by Francesco Guerra. It allows one to construct lower bounds on the entropy of the transmitted message conditional to the received one. Based on heuristic statistical mechanics calculations, we conjecture such bounds to be tight. The result holds for standard irregular ensembles when used over binary-input output-symmetric (BIOS) channels. The method is first developed for Tanner-graph ensembles with Poisson left-degree distribution. It is then generalized to "multi-Poisson" graphs, and, by a completion procedure, to arbitrary degree distribution

arXiv:cs/0407060•IEEE Transactions on Information Theory•Aug 24, 2005•105 citations

Exact solution for the conditional entropy of Poissonian LDPC codes over the Binary Erasure Channel

Satish Babu Korada, Shrinivas Kudekar, Nicolas Macris

We consider communication over a binary erasure channel with low density parity check codes and optimal maximum a posteriori decoding. It is known that the problem of computing the average conditional entropy, over such code ensembles, in the asymptotic limit of large block length is closely related to computing the free energy of a mean field spin glass in the thermodynamic limit. Tentative, but explicit, formulas for these quantities have been derived thanks to the replica method (of spin glass theory) and are generally conjectured to be exact. In this contribution we show that the replica formulas are indeed exact in the case of Poissonian low density parity check ensembles. Our methods use ideas coming from the recent progress in the rigorous analysis of the Sherrington-Kirkpatrick model and their applications to the theory of error correcting codes.

IEEE International Symposium on Information Theory (ISIT)•Jun 1, 2007•19 citations

Parity-check density versus performance of binary linear block codes over memoryless symmetric channels

Igal Sason, Rudiger L. Urbanke

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.

IEEE Transactions on Information Theory•Jun 23, 2003•121 citations

On Universal Properties of Capacity-Approaching LDPC Code Ensembles

Igal Sason

This paper is focused on the derivation of some universal properties of capacity-approaching low-density parity-check (LDPC) code ensembles whose transmission takes place over memoryless binary-input output-symmetric (MBIOS) channels. Properties of the degree distributions, graphical complexity, and the number of fundamental cycles in the bipartite graphs are considered via the derivation of information-theoretic bounds. These bounds are expressed in terms of the target block/bit error probability and the gap (in rate) to capacity. Most of the bounds are general for any decoding algorithm, and some others are proved under belief propagation (BP) decoding. Proving these bounds under a certain decoding algorithm, validates them automatically also under any suboptimal decoding algorithm. A proper modification of these bounds makes them universal for the set of all MBIOS channels which exhibit a given capacity. Bounds on the degree distributions and graphical complexity apply to finite-length LDPC codes and to the asymptotic case of an infinite block length. The bounds are compared with capacity-approaching LDPC code ensembles under BP decoding, and they are shown to be informative and are easy to calculate. Finally, some interesting open problems are considered.

arXiv:0709.0599•IEEE Transactions on Information Theory•Jun 16, 2009•41 citations

Tight bounds for LDPC and LDGM codes under MAP decoding

Andrea Montanari A

A new method for analyzing low density parity check (LDPC) codes and low density generator matrix (LDGM) codes under bit maximum a posteriori probability (MAP) decoding is introduced. The method is based on a rigorous approach to spin glasses developed by Francesco Guerra. It allows to construct lower bounds on the entropy of the transmitted message conditional to the received one. Based on heuristic statistical mechanics calculations, we conjecture such bounds to be tight. The method is illustrated on codes with Poisson left degree distributions when used over binary input output symmetric channels. However, it is arguably much more general. UMR 8549, Unité Mixte de Recherche du Centre National de la Recherche Scientifique et de l ’ Ecole Normale

•Jan 1, 2005•101 citations
Consider communication over a binary-input memoryless output-symmetric channel with low-density parity-check (LDPC) codes and maximumaposteriori(MAP) decoding. The replica method of spin glass theory allows to conjecture an analytic formula for the average input-output conditional entropy per bit in the infinite block length limit. Montanari proved a lower bound for this entropy, in the case of LDPC ensembles with convex check degree polynomial, which matches the replica formula. Here we extend this lower bound to any irregular LDPC ensemble. The new feature of our work is an analysis of the second derivative of the conditional input-output entropy with respect to noise. A close relation arises between this second derivative and correlation or mutual information of codebits. This allows us to extend the realm of the ldquointerpolation method,rdquo in particular, we show how channel symmetry allows to control the fluctuations of the ldquooverlap parametersrdquo.
A new method for analyzing low-density parity-check (LDPC) codes and low-density generator-matrix (LDGM) codes under bit maximum a posteriori probability (MAP) decoding is introduced. The method is based on a rigorous approach to spin glasses developed by Francesco Guerra. It allows one to construct lower bounds on the entropy of the transmitted message conditional to the received one. Based on heuristic statistical mechanics calculations, we conjecture such bounds to be tight. The result holds for standard irregular ensembles when used over binary-input output-symmetric (BIOS) channels. The method is first developed for Tanner-graph ensembles with Poisson left-degree distribution. It is then generalized to "multi-Poisson" graphs, and, by a completion procedure, to arbitrary degree distribution
We consider communication over a binary erasure channel with low density parity check codes and optimal maximum a posteriori decoding. It is known that the problem of computing the average conditional entropy, over such code ensembles, in the asymptotic limit of large block length is closely related to computing the free energy of a mean field spin glass in the thermodynamic limit. Tentative, but explicit, formulas for these quantities have been derived thanks to the replica method (of spin glass theory) and are generally conjectured to be exact. In this contribution we show that the replica formulas are indeed exact in the case of Poissonian low density parity check ensembles. Our methods use ideas coming from the recent progress in the rigorous analysis of the Sherrington-Kirkpatrick model and their applications to the theory of error correcting codes.
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 is focused on the derivation of some universal properties of capacity-approaching low-density parity-check (LDPC) code ensembles whose transmission takes place over memoryless binary-input output-symmetric (MBIOS) channels. Properties of the degree distributions, graphical complexity, and the number of fundamental cycles in the bipartite graphs are considered via the derivation of information-theoretic bounds. These bounds are expressed in terms of the target block/bit error probability and the gap (in rate) to capacity. Most of the bounds are general for any decoding algorithm, and some others are proved under belief propagation (BP) decoding. Proving these bounds under a certain decoding algorithm, validates them automatically also under any suboptimal decoding algorithm. A proper modification of these bounds makes them universal for the set of all MBIOS channels which exhibit a given capacity. Bounds on the degree distributions and graphical complexity apply to finite-length LDPC codes and to the asymptotic case of an infinite block length. The bounds are compared with capacity-approaching LDPC code ensembles under BP decoding, and they are shown to be informative and are easy to calculate. Finally, some interesting open problems are considered.
A new method for analyzing low density parity check (LDPC) codes and low density generator matrix (LDGM) codes under bit maximum a posteriori probability (MAP) decoding is introduced. The method is based on a rigorous approach to spin glasses developed by Francesco Guerra. It allows to construct lower bounds on the entropy of the transmitted message conditional to the received one. Based on heuristic statistical mechanics calculations, we conjecture such bounds to be tight. The method is illustrated on codes with Poisson left degree distributions when used over binary input output symmetric channels. However, it is arguably much more general. UMR 8549, Unité Mixte de Recherche du Centre National de la Recherche Scientifique et de l ’ Ecole Normale