论文标题
高密度平价检查代码的扰动自适应信念传播解码
Perturbed Adaptive Belief Propagation Decoding for High-Density Parity-Check Codes
论文作者
论文摘要
代数代码（例如BCH代码）正在收到更新的兴趣，因为它们的短块长度和低/无误差地板使其对5G无线网络中的超级可靠的低延迟通信（URLLC）有吸引力。本文旨在增强传统的自适应信念繁殖（ABP）解码，这是用于高密度平价 - 检查（HDPC）代数代码的柔软发出（SISO）解码，例如Reed-Solomon（RS）代码，Bose-Chaudhuri-Hocquegengengengengemon（RS）代码。传统ABP的关键思想是稀疏奇偶校验检查矩阵的某些列，该矩阵对应于具有小的log-likelihood-Ratio（LLR）值的最低可靠位。当某些位具有较大的LLR幅度但迹象错误时，这种稀疏策略可能不会是最佳的。在这一观察结果的推动下，我们提出了一个扰动的ABP（P-ABP），将少量的不稳定位和大LLR的不稳定位融入了奇偶校验检查矩阵的稀疏操作中。此外，我们建议采用部分分层调度或混合动态调度，以进一步提高P-ABP的性能。仿真结果表明，与先前的ABP变体相比，我们提出的解码算法可改善误差校正性能和更快的收敛速率。
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-likelihood-ratio (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 parity-check 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.
