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

IEEE International Symposium on Information Theory (ISIT)

Bayesian and non-bayesian methods for joint decoding and detection in the presence of phase noise

Giulio Colavolpe, Alan Barbieri, Giuseppe Caire, N. Bonneau

Published Dec 23, 2004• 7 citations
Publisher

Abstract

This paper proposes new algorithms for joint iterative decoding and parameter estimation in Bayesian and nonBayesian method. The low-density parity-check (LDPC) codes in the presence of phase noise is focused in this paper. The nonBayesian algorithm for an unknown phase channel is based on the application of the expectation-maximization (EM) algorithm. The proposed Bayesian algorithms are obtained as an application of the sum-product algorithm to the factor graph. Comparing the performance of the considered algorithms in the case of LDPC code, the EM-KL (EM-Karhunen-Loeve) algorithm performs better than EM-SW (sliding window) algorithm.

DOI
10.1109/isit.2004.1365166

Related papers

Algorithms for iterative decoding in the presence of strong phase noise

Giulio Colavolpe, Alan Barbieri, Giuseppe Caire

We present two new iterative decoding algorithms for channels affected by strong phase noise and compare them with the best existing algorithms proposed in the literature. The proposed algorithms are obtained as an application of the sum-product algorithm to the factor graph representing the joint a posteriori probability mass function of the information bits given the channel output. In order to overcome the problems due to the presence in the factor graph of continuous random variables, we apply the method of canonical distributions . Several choices of canonical distributions have been considered in the literature. Well-known approaches consist of discretizing continuous variables or treating them as jointly Gaussian, thus obtaining a Kalman estimator. Our first new approach, based on the Fourier series expansion of the phase probability density function, yields better complexity/performance tradeoff with respect to the usual discretized-phase method. Our second new approach, based on the Tikhonov canonical distribution, yields near-optimal performance at very low complexity and is shown to be much more robust than the Kalman method to the placement of pilot symbols in the coded frame. We present numerical results for binary LDPC codes and LDPC-coded modulation, with particular reference to some phase-noise models and coded-modulation formats standardized in the next-generation satellite Digital Video Broadcasting (DVB-S2). These results show that our algorithms achieve near-coherent performance at very low complexity without requiring any change to the existing DVB-S2 standard.

IEEE Journal on Selected Areas in Communications•Sep 1, 2005•272 citations

Efficient iterative decoding of LDPC in the presence of strong phase noise

Shachar Shayovitz, Dan Raphaeli

In this paper we propose a new efficient message passing algorithm for decoding LDPC transmitted over a channel with strong phase noise. The algorithm performs approximate bayesian inference on a factor graph representation of the channel and code joint posterior. The approximate inference is based on an improved canonical model for the messages of the Sum & Product Algorithm, and a method for clustering the messages using the directional statistics framework. The proposed canonical model includes treatment for phase slips which can limit the performance of tracking algorithms. We show simulation results and complexity analysis for the proposed algorithm demonstrating its superiority over some of the current state of the art algorithms.

arXiv:1204.2660•International Symposium on Turbo Codes and Iterative Information Processing•Aug 1, 2012•7 citations

Joint Iterative Decoding and Noise Variance Estimation Based on Expectation-Maximization

Wang Hang-xian

Effects of noise variance estimation on the LDPC decoding performance are investigated.Based on the algorithm of expectation-maximization(EM),a noise variance estimation method is obtained.The method is applied to the BP decoding algorithm.Simulations show that,with the proposed method,the observed performance degradation is less than 0.1 dB when the noise variance is unknown.

•Jan 1, 2007•0 citations

Stochastic decoding of LDPC codes

Saeed Sharifi Tehrani, Warren J. Gross, Shie Mannor

This letter presents the first successful method for iterative stochastic decoding of state-of-the-art low-density parity-check (LDPC) codes. The proposed method shows the viability of the stochastic approach for decoding LDPC codes on factor graphs. In addition, simulation results for a 200 and a 1024 length LDPC code demonstrate the near-optimal performance of this method with respect to sum-product decoding. The proposed method has a significant potential for high-throughput and/or low complexity iterative decoding.

IEEE Communications Letters•Oct 1, 2006•165 citations

Iterative Decoding of Low-Density Parity-Check Codes Over Compound Channels

I. Sutskover, S. Shamai

Iterative Decoding of Low-Density Parity-Check Codes Over Compound Channels We present a setting for decoding of low-density parity-check (LDPC) codes jointly with channel estimation, suitable for transmission over memoryless compound channels. We show that the performance of the combined scheme can be rigorously evaluated by means of density evolution, and focus on density evolution as a tool for designing a channel estimator that matches not only to the channel, but also to the LDPC ensemble, as well. The utility of the concept is exemplified for a compound binary symmetric channel and an unknown-phase additive white Gaussian channel.

IEEE Transactions on Communications•Nov 1, 2005•1 citation

Joint Channel-State Estimation and Decoding of Low-Density Parity-Check Codes on the Two-State Noiseless/Useless BSC Block Interference Channel

Joint Channel-State Estimation and Decoding of Low-Density Parity-Check Codes on the Two-State Noiseless/Useless BSC Block Interference Channel We apply the density-evolution technique to determine the thresholds of low-density parity-check (LDPC) codes when the sum-product algorithm is employed to perform joint channel-state estimation and decoding. The channel considered is the two-state noiseless/useless binary symmetric channel (BSC) block interference channel, where a block of $h$ consecutive symbols shares the same channel state, which is either a noiseless BSC (crossover probability 0) or a useless BSC (crossover probability 1/2). The channel state is selected independently and at random from block to block, according to a known prior distribution. The threshold of the joint channel-state estimation/decoding scheme when used over such a channel is shown to be greatly superior to that of a decoder that makes no attempt to estimate the channel state. These results are also confirmed by simulation. The maximum-likelihood performance of LDPC codes when used over this channel is investigated. Lower bounds on the error exponents of regular LDPC codes, when maximum-likelihood decoded, are shown to be close to the random coding channel-error exponent when the LDPC variable node degree is high.

IEEE Transactions on Communications•Mar 1, 2005•0 citations
This paper proposes new algorithms for joint iterative decoding and parameter estimation in Bayesian and nonBayesian method. The low-density parity-check (LDPC) codes in the presence of phase noise is focused in this paper. The nonBayesian algorithm for an unknown phase channel is based on the application of the expectation-maximization (EM) algorithm. The proposed Bayesian algorithms are obtained as an application of the sum-product algorithm to the factor graph. Comparing the performance of the considered algorithms in the case of LDPC code, the EM-KL (EM-Karhunen-Loeve) algorithm performs better than EM-SW (sliding window) algorithm.
We present two new iterative decoding algorithms for channels affected by strong phase noise and compare them with the best existing algorithms proposed in the literature. The proposed algorithms are obtained as an application of the sum-product algorithm to the factor graph representing the joint a posteriori probability mass function of the information bits given the channel output. In order to overcome the problems due to the presence in the factor graph of continuous random variables, we apply the method of canonical distributions . Several choices of canonical distributions have been considered in the literature. Well-known approaches consist of discretizing continuous variables or treating them as jointly Gaussian, thus obtaining a Kalman estimator. Our first new approach, based on the Fourier series expansion of the phase probability density function, yields better complexity/performance tradeoff with respect to the usual discretized-phase method. Our second new approach, based on the Tikhonov canonical distribution, yields near-optimal performance at very low complexity and is shown to be much more robust than the Kalman method to the placement of pilot symbols in the coded frame. We present numerical results for binary LDPC codes and LDPC-coded modulation, with particular reference to some phase-noise models and coded-modulation formats standardized in the next-generation satellite Digital Video Broadcasting (DVB-S2). These results show that our algorithms achieve near-coherent performance at very low complexity without requiring any change to the existing DVB-S2 standard.
In this paper we propose a new efficient message passing algorithm for decoding LDPC transmitted over a channel with strong phase noise. The algorithm performs approximate bayesian inference on a factor graph representation of the channel and code joint posterior. The approximate inference is based on an improved canonical model for the messages of the Sum & Product Algorithm, and a method for clustering the messages using the directional statistics framework. The proposed canonical model includes treatment for phase slips which can limit the performance of tracking algorithms. We show simulation results and complexity analysis for the proposed algorithm demonstrating its superiority over some of the current state of the art algorithms.
This letter presents the first successful method for iterative stochastic decoding of state-of-the-art low-density parity-check (LDPC) codes. The proposed method shows the viability of the stochastic approach for decoding LDPC codes on factor graphs. In addition, simulation results for a 200 and a 1024 length LDPC code demonstrate the near-optimal performance of this method with respect to sum-product decoding. The proposed method has a significant potential for high-throughput and/or low complexity iterative decoding.
Iterative Decoding of Low-Density Parity-Check Codes Over Compound Channels We present a setting for decoding of low-density parity-check (LDPC) codes jointly with channel estimation, suitable for transmission over memoryless compound channels. We show that the performance of the combined scheme can be rigorously evaluated by means of density evolution, and focus on density evolution as a tool for designing a channel estimator that matches not only to the channel, but also to the LDPC ensemble, as well. The utility of the concept is exemplified for a compound binary symmetric channel and an unknown-phase additive white Gaussian channel.
Joint Channel-State Estimation and Decoding of Low-Density Parity-Check Codes on the Two-State Noiseless/Useless BSC Block Interference Channel We apply the density-evolution technique to determine the thresholds of low-density parity-check (LDPC) codes when the sum-product algorithm is employed to perform joint channel-state estimation and decoding. The channel considered is the two-state noiseless/useless binary symmetric channel (BSC) block interference channel, where a block of hhh consecutive symbols shares the same channel state, which is either a noiseless BSC (crossover probability 0) or a useless BSC (crossover probability 1/2). The channel state is selected independently and at random from block to block, according to a known prior distribution. The threshold of the joint channel-state estimation/decoding scheme when used over such a channel is shown to be greatly superior to that of a decoder that makes no attempt to estimate the channel state. These results are also confirmed by simulation. The maximum-likelihood performance of LDPC codes when used over this channel is investigated. Lower bounds on the error exponents of regular LDPC codes, when maximum-likelihood decoded, are shown to be close to the random coding channel-error exponent when the LDPC variable node degree is high.