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Electronics Letters

Modified on-demand belief propagation algorithm for decoding of low-density parity-check convolutional codes

Yan Liu, Xingkun Wang, Rong-Jie Chen, Yuxuan He

Published Jun 3, 2008• 0 citations
Publisher

Abstract

A modified on-demand belief propagation (BP) algorithm is proposed for decoding of low-density parity-check convolutional codes. The modification improves the efficiency of the on-demand BP algorithm updating the variable information by applying the feedback information at each iteration. A BER performance better than that of the on-demand BP algorithm can be achieved, while the decoding latency is effectively reduced.

DOI
10.1049/el:20080624

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A modified on-demand belief propagation (BP) algorithm is proposed for decoding of low-density parity-check convolutional codes. The modification improves the efficiency of the on-demand BP algorithm updating the variable information by applying the feedback information at each iteration. A BER performance better than that of the on-demand BP algorithm can be achieved, while the decoding latency is effectively reduced.
A novel belief propagation(BP) decoding algorithm for convolutional low-density parity-check codes is proposed.The proposed algorithm raises the efficiency of updating the variable information by applying feedback information at each decoding iteration and employs the weighting factor to reduce the error propagation caused by the cycles in the Tanner graph,thus yielding a faster convergence of the decoding.Simulation results show that an error performance better than that of the existing belief propagation algorithm can be achieved,while the 5/8 decoding delay and the computation complexity are effectively reduced.Compared with the existing BP,the proposed algorithm achieves a gain of 0.16dB at the BER of 10-6 with the same number of iterations.
A feedback belief propagation (BP) decoding algorithm for low-density parity-check convolutional codes is proposed. The proposed algorithm can activate the variable nodes more efficiently by applying feedback decoding at each decoding iteration. Compared with the on-demand BP algorithm, the proposed algorithm has a doubled convergence speed and causes only about half of the decoding delay at similar error performances without any increase of storage requirement. Simulation results show that the proposed algorithm can offer a good trade-off between the error-correcting performance and the decoding complexity.
A low-density parity check (LDPC) code with the belief propagation (BP) or the log-likelihood ratio belief propagation (LLR-BP) can achieve good bit error rate (BER) performance approaching the Shannon limit. When a parity check matrix of the LDPC code has the cycle, the BP and LLR-BP decoding algorithms achieve approximate maximum a posterior probability (MAP) decoding. Although the decoding algorithms are approximate MAP decoding, LDPC codes can achieve very good BER. For the short and middle length LDPC codes, BER and block error rate (BLER) performances are affected by cycle largely. In each iteration, the magnitudes of a posterior LLRs of some bits oscillate owing to cycles. The oscillation is the dominant error factor in the high Eb/Noregion for short and middle length LDPC codes. In this paper, we extend the definition of oscillation to extrinsic LLR (ex-LLR) derived in the bit node process and propose the modified LLR-BP and the modified UMP-BP decoding algorithms. To reduce effects of oscillating ex-LLRs on decoding, for oscillating ex-LLRs, we add the previous ex-LLR to the current ex-LLR. From the computer simulation, we show that for short and middle length LDPC codes, with a simple modification, our proposed decoding algorithms can improve the conventional LLR-BP and UMP-BP decoding algorithms. In particular, we show that the modified UMP-BP decoding algorithm with low complexity can achieve better BER and BLER than the conventional LLR-BP decoding algorithm
According to no-convergence of falsely oscillating iteration in the low-density parity-check(LDPC) decoding at the range from medium to high SNRs(Signal to Noise Ratios),we propose a modified LDPC belief propagation(BP) decoding algorithm,i.e.soft value zero-forcing BP algorithm.By setting extrinsic information of the oscillating iteration bit nodes into zero,the impact on the iteratively decoding from false channel information is greatly reduced.And it also improves the performance of the decoding algorithm.Furthermore,a decision criterion of oscillating iteration nodes is presented to increase the accuracy of the decision.Simulation results show that the proposed algorithm has better decoding performance than that of the BP algorithm with the same iterations at medium and high SNRs.
An improved belief propagation(BP) decoding scheme was put forward to solve the high complexity and low decoding efficiency problem of BP in low density parity check(LDPC) codes.Because different bit nodes and check nodes have different reliability and contribute differently to BP decoding;the proposed decoding scheme employs a differentiate rule based on lawless check number and pseudo posterior probability to evaluate every bit's reliability before each step of iteration;The nodes have little lawless check number and large pseudo posterior probability are supposed to have higher reliability.The bits with high reliability need not to be updated by the decoder.Simulation shows that the proposed BP decoding algorithm can reduce the computational load and increase the decoding efficiency significantly.