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International Symposium on Communications and Information Technologies

An modified beyond belief propagation algorithm over AWGN channel

Yibo Lv, Lin Wang

Published Sep 1, 2013• 2 citations
Publisher

Abstract

Recently one decoding algorithm called as beyond belief propagation (BP) algorithm for LDPC Codes is derived using the knowledge of trapping set and different from standard BP algorithm, whose performance outperforms that of the latter over binary symmetric channel (BSC). However, the former is not suitable for additive white Gaussian noise (AWGN) channel. In this paper how to improve bit error rate (BER) performance of Beyond BP Algorithm is investigated over AWGN channel. It is found that this algorithm can be modified through setting appropriate penalty factor and obtains better performance than BP one over AWGN channel. The simulation results show dependently on choosing penalty factor modified Beyond BP Algorithm outperforms BP one over AWGN channel in high signal noise rate (SNR) region or almost whole SNR one and there are some differences for different code rates. Concretely, for medium or low code rate proposed algorithm can obtain more performance gain relative to high one when short frame is used.

DOI
10.1109/iscit.2013.6645826

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Recently one decoding algorithm called as beyond belief propagation (BP) algorithm for LDPC Codes is derived using the knowledge of trapping set and different from standard BP algorithm, whose performance outperforms that of the latter over binary symmetric channel (BSC). However, the former is not suitable for additive white Gaussian noise (AWGN) channel. In this paper how to improve bit error rate (BER) performance of Beyond BP Algorithm is investigated over AWGN channel. It is found that this algorithm can be modified through setting appropriate penalty factor and obtains better performance than BP one over AWGN channel. The simulation results show dependently on choosing penalty factor modified Beyond BP Algorithm outperforms BP one over AWGN channel in high signal noise rate (SNR) region or almost whole SNR one and there are some differences for different code rates. Concretely, for medium or low code rate proposed algorithm can obtain more performance gain relative to high one when short frame is used.
The decoding of the LDPC codes by the belief propagation(BP) algorithm for the additive white Gaussian noise(AWGN) channel requires an accurate estimation of the signal-to-noise ratio(SNR) for the computation of the a posteriori probabilities(APPs) of the received bits that serve as the decoder inputs.An erroneous estimation of SNR at the decoder is referred to as SNR mismatch.In this paper,we study the effects of the SNR mismatch on the bit error rate(BER) performance of the LDPC codes for the AWGN channel.The correct-factor function is achieved by approximating the check node update equation of the BP algorithm.It is concluded that the effect of SNR under-estimation on decoding is more sensitive than SNR over-estimation based on the correct-factor function.The simulation results also indicate the conclsion.
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.
Designing realizable Maximum Likelihood (ML) decoders for low-density parity-check (LDPC) codes has always been a challenging problem. Maxwell decoder is a well-known ML decoder for LDPC codes over a binary erasure channel (BEC). Based on the concept used in the Maxwell decoder, it may be possible to design near-ML decoders for LDPC codes over an additive white Gaussian noise (AWGN) channel. In this paper, we introduce a novel idea called channel transformation, which will be used as a bridge linking a BEC channel and an AWGN channel. With the transformation, we further propose a near-ML decoder for LDPC codes sent over an AWGN channel. The proposed near-ML decoder, combining a Belief Propagation (BP) decoder and a Maxwell decoder, is called the BP-Maxwell (BM) decoder. Simulation results show that compared with a BP decoder, the BM decoding algorithm can accomplish better frame error rates (FERs) by eliminating many of the error frames with a smaller percentage of error bits. Thus, the BM decoder is a step closer to the ML decoder compared with the BP decoder.
Designing a realizable maximum likelihood(ML) decoder for low-density parity-check(LDPC) codes is always a challenging work over an additive white Gaussian noise(AWGN) channel.Although Maxwell decoder is well known for its excellent performance over the binary erasure channel(BEC),it seems that generalizing this algorithm for other channels is difficult.This paper introduces an idea called channel transformation which could realize the conversation between two different channels.A Maxwell decoder is applied to an AWGN channel.In terms of this method,and a joint decoder-BP-Maxwell(BM) decoder is proposed which combines a belief propagation(BP) decoder and a Maxwell decoder,to reduce the gap to the ML decoder in performance.Simulation results show that the BM decoding algorithm could break most small trapping sets to accomplish a lower frame error rate(FER).Moreover it also could eliminate most of the small-scale errors compared with a BP decoder.
Under the condition of the Additive White Gaussian Noise(AWGN) channel,both the hard and soft decision decoding algorithms suitable for LDPC codes are simulated and comparatively analyzed based on analyzing the decoding algorithm for LDPC codes.The variable message correlation for Log-Likelihood-Ratio Belief-Propagation(LLR-BP) algorithm in soft decision algorithm is decreased by introducing a multiplicative correction factor.The simulation results show that the decoding error-correction performance of the modified LLR-BP algorithm is greatly improved with little increase of computing complexity.
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.