Algebraic codes such as BCH code are receiving renewed interest as their short block lengths and low/no error floors make them attractive for ultra-reliable low-latency communications (URLLC) in 5G wireless networks. This paper aims at enhancing the traditional adaptive belief propagation (ABP) decoding, which is a soft-in-soft-out (SISO) decoding for high-density parity-check (HDPC) algebraic codes, such as Reed-Solomon (RS) codes, Bose-Chaudhuri-Hocquenghem (BCH) codes, and product codes. The key idea of traditional ABP is to sparsify certain columns of the parity-check matrix corresponding to the least reliable bits with small log-likelihoodratio (LLR) values. This sparsification strategy may not be optimal when some bits have large LLR magnitudes but wrong signs. Motivated by this observation, we propose a Perturbed ABP (P-ABP) to incorporate a small number of unstable bits with large LLRs into the sparsification operation of the paritycheck matrix. In addition, we propose to apply partial layered scheduling or hybrid dynamic scheduling to further enhance the performance of P-ABP. Simulation results show that our proposed decoding algorithms lead to improved error correction performances and faster convergence rates than the prior-art ABP variants.

中文翻译：

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高密度奇偶校验码的扰动自适应置信度传播解码

代数码（如 BCH 码）由于其较短的块长度和低/无误码使它们对 5G 无线网络中的超可靠低延迟通信 (URLLC) 具有吸引力，因此重新引起了人们的兴趣。本文旨在增强传统的自适应置信传播 (ABP) 解码，这是一种用于高密度奇偶校验 (HDPC) 代数码的软入软出 (SISO) 解码，例如 Reed-Solomon (RS)代码、Bose-Chaudhuri-Hocquenghem (BCH) 代码和产品代码。传统 ABP 的关键思想是稀疏化奇偶校验矩阵的某些列，这些列对应于具有较小对数似然比 (LLR) 值的最不可靠位。当某些位具有较大的 LLR 幅度但符号错误时，这种稀疏化策略可能不是最佳的。受此观察启发，我们提出了一种扰动 ABP（P-ABP），将少量具有大 LLR 的不稳定位合并到奇偶校验矩阵的稀疏化操作中。此外，我们建议应用部分分层调度或混合动态调度来进一步提高 P-ABP 的性能。仿真结果表明，我们提出的解码算法比现有技术的 ABP 变体提高了纠错性能和更快的收敛速度。