Conference on Information Sciences and Systems

Noise adaptive LDPC decoding using particle filter

Lijuan Cui, Shuang Wang, Samuel S. M. Cheng, Qiang Wu

Published Mar 1, 2009• 11 citations

Abstract

Belief propagation (BP) is a powerful algorithm to decode the low-density parity check (LDPC) codes over the additive white Gaussian noise (AWGN) channel. The traditional BP algorithm cannot adapt efficiently to the statistical change of the AWGN channel. Particle filter is a algorithm to estimate a variable of interest as it evolves over time. In this paper, we use particle filter to estimate the noise power and feed back to the BP algorithm in real time. We found that compared with the traditional BP algorithm with fixed estimated noise power, BP algorithm based on particle filter not only give a good real-time estimate for the channel noise, but also achieve a lower decoding error rate.

DOI: 10.1109/ciss.2009.5054686

Related papers

Noise Adaptive LDPC Decoding Using Particle Filtering

Shuang Wang, Lijuan Cui, Samuel S. M. Cheng +3 more

Belief propagation (BP) is a powerful algorithm to decode low-density parity check (LDPC) codes over additive white Gaussian noise (AWGN) channels. However, the traditional BP algorithm cannot adapt efficiently to the statistical change of SNR in an AWGN channel. This paper proposes an adaptive scheme that incorporates a particle filtering (PF) algorithm into the BP based LDPC decoding process. The proposed scheme is capable to perform online estimation of time-varying SNR at the bit-level and enhance the BP decoding performance simultaneously.

IEEE Transactions on CommunicationsJan 21, 201111 citations

Noise Adaptive LDPC Decoding Using Expectation Propagation

Shuang Wang, Lijuan Cui, Samuel S. M. Cheng

Belief propagation (BP) is a powerful algorithm to decode low- density parity check (LDPC) codes over additive white Gaussian noise (AWGN) channels. However, the traditional BP algorithm cannot adapt efficiently to the statistical change of SNR in an AWGN channel. This paper proposes an adaptive scheme that incorporates expectation propagation (EP) into the BP based LDPC decoding process. The proposed scheme is able to perform online estimation of both stationary and time-varying SNR at the bit-level, and enhance the BP decoding performance simultaneously. Moreover, the proposed EP estimator shows a very fast convergence speed, and the additional computational overhead of the proposed decoder is less than 10% of the standard BP decoder.

GlobecomDec 1, 20113 citations

Online SNR statistic estimation for LDPC decoding over AWGN channel using laplace propagation

Lijuan Cui, Shuang Wang, Samuel S. M. Cheng

Estimating accurately channel noise has significant impact on the performance of channel coding. In this paper, we present a low complexity online estimator at the decoder side for low-density parity check (LDPC) codes over additive white Gaussian noise (AWGN) channels with a time-varying noise. The proposed algorithm is obtained by modeling the channel statistic as a Bayesian inference problem on a factor graph. Since BP algorithm cannot adapt efficiently to the statistical change of SNR in an AWGN channel, we apply the Laplace propagation (LP) algorithm on the proposed algorithm. Through simulations, we show that the proposed algorithm can simultaneously decode LDPC codes and estimate the time-varying SNR at the bit-level, which enhance the BP decoding performance. Moreover, the proposed LP estimator shows a very low computational complexity.

GlobecomDec 1, 20123 citations

A high‐performance belief propagation decoding algorithm for codes with short cycles

Ali Karimi-lenji, Monireh Houshmand, Fahimeh Zarmehi

Summary The simplicity of decoding is one of the most important characteristics of the low density parity check (LDPC) codes. Belief propagation (BP) decoding algorithm is a well‐known decoding algorithm for LDPC codes. Most LDPC codes with long lengths have short cycles in their Tanner graphs, which reduce the performance of the BP algorithm. In this paper, we present 2 methods to improve the BP decoding algorithm for LDPC codes. In these methods, the calculation of the variable nodes is controlled by using “multiplicative correction factor” and “additive correction factor.” These factors are obtained for 2 separate channels, namely additive white Gaussian noise (AWGN) and binary symmetric channel (BSC), as 2 functions of code and channel parameters. Moreover, we use the BP‐based method in the calculation of the check nodes, which reduces the required resources. Simulation results show the proposed algorithm has better performance and lower decoding error as compared to BP and similar methods like normalized‐BP and offset‐BP algorithms.

International Journal of Communication SystemsFeb 28, 20172 citations

Performance of Belief Propagation for Decoding LDPC Codes in the Presence of Channel Estimation Error

Hamid Saeedi, Amir H. Banihashemi

In this paper, we investigate the performance of the belief propagation (BP) algorithm for decoding low-density parity-check codes over the additive white Gaussian noise channel when there is an incorrect estimate of the channel signal-to-noise ratio (SNR) (referred to as "SNR mismatch") at the decoder. At the extremes for over- and underestimation of SNR, the performance of BP tends to that of min-sum algorithm and the channel bit-error rate, respectively. Our results for regular codes indicate that the sensitivity to mismatch increases by increasing the variable-node degree and by decreasing the check-node degree. The effect of variable-node degree, however, appears to be more profound, such that at a given rate, the codes with the smallest variable and check degrees are more robust against SNR mismatch. For irregular codes, by comparing the thresholds of a few ensembles, we demonstrate that the ensemble which performs better in the absence of mismatch can perform worse in the presence of it. To obtain our asymptotic results, we propose a computationally efficient method based on the Gaussian approximation of density evolution in the presence of SNR mismatch. We also show that the asymptotic results are consistent with simulation results for codes with finite block lengths

IEEE Transactions on CommunicationsJan 1, 200743 citations

Shuffled belief propagation decoding based on variable nodes reliability

Wang Haiqua

This paper proposes a new group shuffled belief propagation(BP)decoding of low-density parity-check(LDPC)codes,which divides check nodes or variable nodes into groups based on the reliability of the initial channel message.The bit error rate and the average number of iterations are analyzed with additive white Gaussian noise(AWGN)channel.Simulations verify that the proposed decoding strategy greatly improves the decoding performance.

Chinese Journal of Radio ScienceJan 1, 20150 citations