Error correction in interference-limited wireless systems

TL;DR

This paper proposes two novel error correction decoding methods tailored for interference-limited wireless systems modeled with alpha-stable noise, significantly improving performance over Gaussian assumptions.

Contribution

It introduces two approaches that incorporate alpha-stable noise characteristics into decoding algorithms, enhancing error correction in interference-limited wireless environments.

Findings

Both methods outperform Gaussian-based decoding by 2-3 dB.

Similar block error rate performance for both approaches.

Effective in 5G NR CA-Polar and CRC codes.

Abstract

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.