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IEEE International Conference on Communication Technology (ICCT)

A low-complexity min-sum decoding algorithm for LDPC codes

Huan Li, Jing Guo, Chen Guo, Donglin Wang

Published Oct 1, 2017• 16 citations
Publisher

Abstract

This paper proposes a low-complexity LDPC decoding algorithm with simplified check nodes updating. The proposed algorithm simplifies the result of the second-minimum in check nodes based on the first-minimum computation instead of computing it directly. In order to obtain the approximate result, effective corrected coefficients are utilized, which can reduce the complexity by eliminating the complex computations. Complexity analysis is provided and the results indicate that the complexity of the proposed algorithm is much lower than the general NMS algorithm and MS-based simplified algorithms. Simulation results show that the performance of the proposed algorithm can closely match the NMS algorithm with the same number of iterations.

DOI
10.1109/icct.2017.8359612

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This letter presents a heuristic technique for simplifying the parity-check node operation in a relaxed min-sum iterative decoder. The proposed decoder eliminates the second-minimum computation in check nodes, which allows broadcasting the same output to all neighboring variable nodes to alleviate routing problem in VLSI implementations of low-density parity check (LDPC) decoders. The second-minimum, when required, is emulated by adding an offset to the first-minimum. The proposed relaxed decoder also uses a relaxation factor equal to 0.5 to simplify variable nodes. Simulation results for two LDPC codes show the proposed decoding algorithm with only 4-bit quantization closely matches the performance of floating-point normalized/offset min-sum and sum-product decoders in the waterfall region.
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Reduced-Complexity Decoding of LDPC Codes Various log-likelihood-ratio-based belief-propagation (LLR- BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representation of the decoding computations are shown to achieve a reduction in complexity, by simplifying the check-node update or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from a performance, latency, computational complexity, and memory-requirement perspective.