Improved Layered Minimum Sum Decoding Algorithm Based on Overestimation

Liu Yu, Lin Bai, Yaohui Hao
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Abstract

Quasi-cyclic low-density parity-check (QC-LDPC) codes are linear block codes with performance close to the Shannon limit. The layered minimum sum (LMS) decoding algorithm speeds up the decoding convergence by layering the check matrix and updating the nodes according to the layers. However, the MS algorithm has the problem of over estimation and affects the decoding performance due to its simplified strategy of MS algorithm updating the message of check nodes. Therefore, this paper proposes a layered minimum sum decoding algorithm based on overestimation. By setting the correction threshold and updating the check nodes by the conditions, the decoding performance is improved. When the code length is 2048 and the bit error rate is , the proposed algorithm can improve the decoding convergence speed by about 25% and obtain a coding gain of about 0.1 dB.
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基于过估计的改进分层最小和译码算法
准循环低密度奇偶校验码是一种性能接近香农极限的线性分组码。分层最小和(LMS)译码算法通过对校验矩阵进行分层并按层更新节点来加快译码收敛速度。然而,由于MS算法对检查节点的消息更新策略进行了简化,存在过估计的问题,影响了译码性能。为此,本文提出了一种基于过估计的分层最小和译码算法。通过设置纠错阈值,并根据条件更新校验节点,提高译码性能。当码长为2048,误码率为时,该算法可将译码收敛速度提高约25%,获得约0.1 dB的编码增益。
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