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

Problems of Information Transmission

Asymptotic bounds on the decoding error probability for two ensembles of LDPC codes

Pavel S. Rybin, Victor Zyablov

Published Jul 1, 2015• 1 citation
Publisher

Abstract

Two ensembles of low-density parity-check (LDPC) codes with low-complexity decoding algorithms are considered. The first ensemble consists of generalized LDPC codes, and the second consists of concatenated codes with an outer LDPC code. Error exponent lower bounds for these ensembles under the corresponding low-complexity decoding algorithms are compared. A modification of the decoding algorithm of a generalized LDPC code with a special construction is proposed. The error exponent lower bound for the modified decoding algorithm is obtained. Finally, numerical results for the considered error exponent lower bounds are presented and analyzed.

DOI
10.1134/s0032946015030011

Related papers

Estimation of the exponent of the decoding error probability for a special generalized LDPC code

Victor Zyablov, Pavel S. Rybin

A special construction of a generalized low-density parity-check (LDPC) code and a low-complexity algorithm for his code decoding are proposed. A lower estimate of the exponent of the decoding error probability is obtained for the considered code and the decoding algorithm. This estimate leads the conclusion that, in an ensemble of considered LDPC codes, there are codes with rates as high as the code capacity and the exponent of the decoding error probability exceeds zero.

Journal of Communications Technology and Electronics•Aug 1, 2012•4 citations

On the Error Exponents of Capacity Approaching Construction of LDPC code

Pavel S. Rybin, Alexey Frolov

In this paper we consider low-density parity-check (LDPC) codes with special construction. We obtain the lower-bounds on the error exponents for these codes under proposed low-complexity decoding algorithm and under a well known maximum likelihood decoding algorithm. We show that such LDPC code with special construction exists, that the error probability of the low-complexity decoding algorithm exponentially decreases with the code length for all code rates below the channel capacity. We also show that obtained lower-bound on the error exponent under the maximum likelihood decoding almost reaches the lower-bound on the error exponent of good linear codes under the maximum likelihood decoding. The error exponents are computed numerically for different code parameters.

International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)•Nov 1, 2018•1 citation

Bounds on the maximum-likelihood decoding error probability of low-density parity-check codes

G. Miller, David Burshtein

We derive both upper and lower bounds on the decoding error probability of maximum-likelihood (ML) decoded low-density parity-check (LDPC) codes. The results hold for any binary-input symmetric-output channel. Our results indicate that for various appropriately chosen ensembles of LDPC codes, reliable communication is possible up to channel capacity. However, the ensemble averaged decoding error probability decreases polynomially, and not exponentially. The lower and upper bounds coincide asymptotically, thus showing the tightness of the bounds. However, for ensembles with suitably chosen parameters, the error probability of almost all codes is exponentially decreasing, with an error exponent that can be set arbitrarily close to the standard random coding exponent.

IEEE Transactions on Information Theory•Jan 1, 2001•157 citations

Error Exponents of LDPC Codes under Low-Complexity Decoding

Pavel S. Rybin, Kirill Andreev, Victor Zyablov

This paper deals with the specific construction of binary low-density parity-check (LDPC) codes. We derive lower bounds on the error exponents for these codes transmitted over the memoryless binary symmetric channel (BSC) for both the well-known maximum-likelihood (ML) and proposed low-complexity decoding algorithms. We prove the existence of such LDPC codes that the probability of erroneous decoding decreases exponentially with the growth of the code length while keeping coding rates below the corresponding channel capacity. We also show that an obtained error exponent lower bound under ML decoding almost coincide with the error exponents of good linear codes.

Entropy•Feb 22, 2021•5 citations

Bounds on the maximum likelihood decoding error probability of low density parity check codes

G. Miller, D. Burshtein

We derive bounds on the error probability of ML decoded LDPC (low density parity check) codes, for any binary-input symmetric-output channel. For appropriately chosen ensembles of LDPC codes, reliable communication is possible up to channel capacity. The lower and upper bounds coincide asymptotically, indicating a polynomially decreasing ensemble averaged error probability. For ensembles with suitably chosen parameters, the error probability of almost all codes is exponentially decreasing. Furthermore, the error exponent can be set arbitrarily close to the standard random coding exponent.

IEEE International Symposium on Information Theory (ISIT)•Nov 7, 2002•7 citations

Asymptotic Enumeration Methods for Analyzing LDPC Codes

David Burshtein, G. Miller

We show how asymptotic estimates of powers of polynomials with nonnegative coefficients can be used in the analysis of low-density parity-check (LDPC) codes. In particular, we show how these estimates can be used to derive the asymptotic distance spectrum of both regular and irregular LDPC code ensembles. We then consider the binary erasure channel (BEC). Using these estimates we derive lower bounds on the error exponent, under iterative decoding, of LDPC codes used over the BEC. Both regular and irregular code structures are considered. These bounds are compared to the corresponding bounds when optimal (maximum-likelihood (ML)) decoding is applied.

IEEE Transactions on Information Theory•Jun 1, 2004•214 citations
Two ensembles of low-density parity-check (LDPC) codes with low-complexity decoding algorithms are considered. The first ensemble consists of generalized LDPC codes, and the second consists of concatenated codes with an outer LDPC code. Error exponent lower bounds for these ensembles under the corresponding low-complexity decoding algorithms are compared. A modification of the decoding algorithm of a generalized LDPC code with a special construction is proposed. The error exponent lower bound for the modified decoding algorithm is obtained. Finally, numerical results for the considered error exponent lower bounds are presented and analyzed.
In this paper we consider low-density parity-check (LDPC) codes with special construction. We obtain the lower-bounds on the error exponents for these codes under proposed low-complexity decoding algorithm and under a well known maximum likelihood decoding algorithm. We show that such LDPC code with special construction exists, that the error probability of the low-complexity decoding algorithm exponentially decreases with the code length for all code rates below the channel capacity. We also show that obtained lower-bound on the error exponent under the maximum likelihood decoding almost reaches the lower-bound on the error exponent of good linear codes under the maximum likelihood decoding. The error exponents are computed numerically for different code parameters.
We derive both upper and lower bounds on the decoding error probability of maximum-likelihood (ML) decoded low-density parity-check (LDPC) codes. The results hold for any binary-input symmetric-output channel. Our results indicate that for various appropriately chosen ensembles of LDPC codes, reliable communication is possible up to channel capacity. However, the ensemble averaged decoding error probability decreases polynomially, and not exponentially. The lower and upper bounds coincide asymptotically, thus showing the tightness of the bounds. However, for ensembles with suitably chosen parameters, the error probability of almost all codes is exponentially decreasing, with an error exponent that can be set arbitrarily close to the standard random coding exponent.
This paper deals with the specific construction of binary low-density parity-check (LDPC) codes. We derive lower bounds on the error exponents for these codes transmitted over the memoryless binary symmetric channel (BSC) for both the well-known maximum-likelihood (ML) and proposed low-complexity decoding algorithms. We prove the existence of such LDPC codes that the probability of erroneous decoding decreases exponentially with the growth of the code length while keeping coding rates below the corresponding channel capacity. We also show that an obtained error exponent lower bound under ML decoding almost coincide with the error exponents of good linear codes.
We derive bounds on the error probability of ML decoded LDPC (low density parity check) codes, for any binary-input symmetric-output channel. For appropriately chosen ensembles of LDPC codes, reliable communication is possible up to channel capacity. The lower and upper bounds coincide asymptotically, indicating a polynomially decreasing ensemble averaged error probability. For ensembles with suitably chosen parameters, the error probability of almost all codes is exponentially decreasing. Furthermore, the error exponent can be set arbitrarily close to the standard random coding exponent.
We show how asymptotic estimates of powers of polynomials with nonnegative coefficients can be used in the analysis of low-density parity-check (LDPC) codes. In particular, we show how these estimates can be used to derive the asymptotic distance spectrum of both regular and irregular LDPC code ensembles. We then consider the binary erasure channel (BEC). Using these estimates we derive lower bounds on the error exponent, under iterative decoding, of LDPC codes used over the BEC. Both regular and irregular code structures are considered. These bounds are compared to the corresponding bounds when optimal (maximum-likelihood (ML)) decoding is applied.