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arXiv 2410.22650cs.ITeess.SPmath.IT
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干扰受限无线系统中的纠错

Error correction in interference-limited wireless systems

Charles Wiame, Ken R. Duffy, Muriel Médard

AI总结:

针对干扰受限无线系统中的α稳定噪声纠错问题,基于GRAND框架提出两种ORBGRAND改进译码方案,在5G NR相关码型下性能较高斯假设译码提升2-3dB。

摘要:

我们提出了一种存在加性α稳定噪声时的纠错译码新方法,α稳定噪声是干扰受限无线系统的建模噪声。若不对译码算法做修改,将α稳定分布当作高斯分布处理会导致显著的性能损失。基于猜测随机加性噪声译码(Guessing Random Additive Noise Decoding, GRAND),我们研究了两种方案。第一种方案在计算作为有序可靠比特GRAND(Ordered Reliability Bits GRAND, ORBGRAND)输入的对数似然比(log-likelihood ratios, LLRs)时,考虑了α稳定噪声的特性。第二种方案基于一款ORBGRAND变体,该变体最初为应对干扰设计,会将离群LLR视为删除。由此得到的混合纠错删译码器可通过ORBGRAND纠错,通过高斯消元纠正删除。两种方案的块错误率(block error rate, BLER)性能相近。对于[128,112] 5G NR CA-Polar码和CRC码,两者的性能均比假设LLR来自高斯噪声的译码方案优2至3 dB。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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