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International Conference on Communications

Comparison of reweighted message passing algorithms for LDPC decoding

Henk Wymeersch, Federico Penna, Vladimir Savić, J. Zhao

Published Jun 1, 2013• 1 citation
Publisher

Abstract

Low density parity check (LDPC) codes can be decoded with a variety of decoding algorithms, offering a trade-off in terms of complexity, latency, and performance. We describe seven distinct LDPC decoders and provide a performance comparison for a practical regular LDPC code. Our simulations indicate that the best performance/latency trade-off is achieved by one version of the reweighted max-product decoder. When latency is not an issue, the traditional sum-product decoder yields the best performance.

DOI
10.1109/icc.2013.6655048

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Low density parity check (LDPC) codes are one of the most popular channel codes.Based on traditional sum-product and max-product algorithms, various modified algorithms are tried to improve the performance of LDPC codes in terms of error rate, complexity and latency.Uniformly rweighted belief propagation (URW-BP) algorithm can offer better performance than traditional algorithm especially for regular LDPC codes.linear programming (LP) decoder is also competitive to traditional message passing algorithm.We describe two traditional message passing decoders, four reweighted message passing decoders based on URW-BP and LP decoder in this paper.Reweighted decoders can outperform traditional decoders especially when iteration number is limited.Considering complexity and latency, reweighted max product decoder of version 2 (R-MPD-II) is the most promising decoder.CONTENTS 4.3 Short regular LDPC codes . . . . . . . .
The near-capacity performance and effective decoding algorithms of Low-Density Parity-Check (LDPC) codes have made them a mainstay of contemporary error correction. With an emphasis on iterative techniques like the Belief Propagation (BP) algorithm, the Min-Sum algorithm, and their variations, this paper offers a thorough analysis of decoding algorithms for LDPC codes. We investigate the trade-offs between various algorithmic techniques’ computational complexity, decoding latency, and error correction performance. Particular focus is placed on new methods and hardware-friendly algorithms that are tailored for next-generation communication systems, such 5 G and beyond.
Low-density parity-check (LDPC) codes perform very close to capacity for long lengths on several channels. However, the amount of memory (fixed-point numbers that need to be stored) required for implementing the message-passing algorithm increases linearly as the number of edges in the graph increases. In this letter, we propose a decoding algorithm for decoding LDPC codes that reduces the memory requirement at the decoder. The proposed decoding algorithm can be analyzed using density evolution; further, we show how to design good LDPC codes using this. Results show that this algorithm provides almost the same performance as the conventional sum-product decoding of LDPC codes.
As for the sum-product decoding algorithm of low density parity check(LDPC) codes,a difference-based decoding method is proposed.The main idea is shown as follows: the messages passed on the bipartite graph of LDPC are based on the probability differences,and the updates of both check nodes and message nodes are carried out on some special addition domain.As for the update of check nodes,only some lowest differences can be selected for reducing computational complexity.In contrast with the traditional log-likelyhood-ratio-based(LLR-based) decoding method,the proposed method has much lower complexity,while it has no obvious loss of performance and convergence.