We introduce a novel approach to error correction decoding in the presence of additive alpha-stable noise, which serves as a model of interference-limited wireless systems. In the absence of modifications to decoding algorithms, treating alpha-stable distributions as Gaussian results in significant performance loss. Building on Guessing Random Additive Noise Decoding (GRAND), we consider two approaches. The first accounts for alpha-stable noise in the evaluation of log-likelihood ratios (LLRs) that serve as input to Ordered Reliability Bits GRAND (ORBGRAND). The second builds on an ORBGRAND variant that was originally designed to account for jamming that treats outlying LLRs as erasures. This results in a hybrid error and erasure correcting decoder that corrects errors via ORBGRAND and corrects erasures via Gaussian elimination. The block error rate (BLER) performance of both approaches are similar. Both outperform decoding assuming that the LLRs originated from Gaussian noise by 2 to 3 dB for [128,112] 5G NR CA-Polar and CRC codes.

翻译：本文提出了一种在加性α稳定噪声存在下进行纠错解码的新方法，该噪声模型用于描述干扰受限的无线系统。若不对解码算法进行修改，将α稳定分布视为高斯分布会导致显著的性能损失。基于猜测随机加性噪声解码（GRAND）框架，我们探讨了两种方法。第一种方法在计算对数似然比（LLR）时考虑α稳定噪声特性，并将LLR作为有序可靠性比特GRAND（ORBGRAND）的输入。第二种方法基于ORBAND的一种变体，该变体最初设计用于处理干扰场景，通过将异常LLR视为擦除来处理。由此构建了一种混合纠错与擦除校正解码器：通过ORBGRAND纠正错误，通过高斯消元法纠正擦除。两种方法的块错误率（BLER）性能相近。对于[128,112] 5G NR CA-Polar和CRC编码，在假设LLR源于高斯噪声的解码基准上，两种方法均实现了2至3 dB的性能提升。