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International Conference on Wireless Communications and Signal Processing (WCSP)

Pruning Path Min-Sum Decoding Algorithm for High-Rate Non-Binary LDPC Codes

Liyuan Song, Hanxiang Yu, Xiaosong Zhang, Ruoyang Qi, Yi Wu

Published Oct 24, 2024• 0 citations
Publisher

Abstract

The fixed path Min-Sum (FMS) algorithm efficiently decodes non-binary LDPC codes with low/moderate code rates, but suffers significant performance degradation for high code rates. This paper enhances its check node (CN) update by selecting deviation paths of high reliability according to their correct probabilities and positions over the trellis of a CN. Moreover, a path pruning strategy is introduced during the CN update to reduce the complexity of path search. Analysis results indicate that the proposed pruning path Min-Sum (PMS) algorithm efficiently decodes high-rate non-binary LDPC codes.

DOI
10.1109/wcsp62071.2024.10827053

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The fixed path Min-Sum (FMS) algorithm efficiently decodes non-binary LDPC codes with low/moderate code rates, but suffers significant performance degradation for high code rates. This paper enhances its check node (CN) update by selecting deviation paths of high reliability according to their correct probabilities and positions over the trellis of a CN. Moreover, a path pruning strategy is introduced during the CN update to reduce the complexity of path search. Analysis results indicate that the proposed pruning path Min-Sum (PMS) algorithm efficiently decodes high-rate non-binary LDPC codes.
This paper reduces the complexity of decoding non-binary low-density parity-check (LDPC) codes by set partition. In the check node update, the input vectors are partitioned into several sets such that different elements in the virtual matrix enjoy various computational strategies. As a result, the proposed algorithm achieves high computational efficiency by setting strategies according to the correct probability of these elements. Simulation results indicate that it significantly decreases the complexity of check node update with negligible performance loss.
In the check node (CN) update of non-binary message-passing algorithms, each element of reliability vectors takes the same computational complexity. However, our analysis indicates that various elements in the same vector have various correct probabilities, thus have different contributions to error performance. In order to match computational complexity with correct probability, all elements in a vector are partitioned into different sets. For the extended min-sum (EMS) decoding, various strategies are applied for sets according to their correct probability. For the trellis-based EMS decoding, it is interesting that set partition only involves fixed paths, thus it does not need to search over the whole trellis of a CN. Complexity analysis and simulation results show that the proposed algorithms efficiently decode non-binary low-density parity-check codes, including ultra-sparse ones.
Low-Density Parity-Check (LDPC) code is a linear block code approaching the Shannon limit. The improvement of the decoding performance of LDPC codes is a hot research direction today. To improve the decoding performance of the self-correcting min-sum (SCMS) algorithm, an improved self-correcting min-sum (ISCMS) algorithm is proposed based on this algorithm. In the iterative decoding process of the ISCMS algorithm, the update rule of the check node (CN) is optimized to the update rule of the CN of the IOMS algorithm, which improves the accuracy of decoding from the processing of the CN. Simulation results show that the proposed ISCMS algorithm has better error performance and convergence performance than the SCMS algorithm with a small increase in computational complexity.
During the check node (CN) update, the elements of input message vectors are redundant for the output message vectors. Hence, in this paper, we exactly select from the input message vectors the elements, which really have contributions to the error-correction performance and constitute the minimum set for the CN update. With adoption of the forward and backward (FB) scheme, an adaptive minimum-set min-sum algorithm (AMSA) is proposed to reduce the computation complexity of the FB process. In order to concurrently update the output vectors belonging to the same CN, we present a parallel minimum-set min-sum algorithm (PMSA) with lower memory complexity than the AMSA. Compared with the min-sum algorithms, the two proposed minimum-set based algorithms introduce no error performance loss.
Low Density Parity Check (LDPC) codes over a Galois Field GF(q) can provide significantly better error- correcting quality than binary LDPC codes with moderate code length. However, the computational complexity and memory requirement of nonbinary codes are much higher, limiting the application of non- binary LDPC codes. In this paper, we propose an adaptive message truncation algorithm for non-binary LDPC decoding, guided by the estimated code error rates. Compared to the previous fixed message truncation method, it can cut the messages adaptively, and therefore provide better decoding quality and computation complexity reduction. To further reduce the computation, we propose another adaptive check node update algorithm, simplifying the decoding by reducing the number of check nodes updating. Our simulation results demonstrate that by combining these two algorithms together, the average message size can be reduced greatly and good decoding quality is achieved, at a little cost of iterations. Compared to the existing truncation algorithms, our approach can reduce the order of complexity to O(nalog2(na)) (where nais messages size), with less performance degradation.
Recently low-density parity check (LDPC) codes have been applied in more areas, and the research of non-binary LDPC codes has become more significant. In this paper, we establish a dynamic scheduling strategy to reduce complexity of non-binary LDPC codes decoding, and choose extended min-sum algorithm as the model. The dynamic scheduling strategy is based on the idea of nodes reliability. When decoding, we select a part of check nodes to update messages in real time according to the reliability rules, and only the variable nodes getting new messages can participate in the message propagation. Compared with usual standard flooding scheduling, this dynamic scheduling strategy can reduce decoding complexity effectively. We also optimize the message of variable nodes according to reliability, which ensure that the error correction performance of decoding algorithm is not affected. Our simulations over a BPSK-AWGN channel show that this dynamic scheduling strategy can reduce complexity in non-binary LDPC codes belief propagation decoding without affecting error correction performance.