GRAND for Fading Channels using Pseudo-soft Information
Abstract
Guessing random additive noise decoding (GRAND) is a universal maximum-likelihood decoder that recovers code-words by guessing rank-ordered putative noise sequences and inverting their effect until one or more valid code-words are obtained. This work explores how GRAND can leverage additive-noise statistics and channel-state information in fading channels. Instead of computing per-bit reliability information in detectors and passing this information to the decoder, we propose leveraging the colored noise statistics following channel equalization as pseudo-soft information for sorting noise sequences. We investigate the efficacy of pseudo-soft information extracted from linear zero-forcing and minimum mean square error equalization when fed to a hardware-friendly soft-GRAND (ORBGRAND). We demonstrate that the proposed pseudo-soft GRAND schemes approximate the performance of state-of-the-art decoders of CA-Polar and BCH codes that avail of complete soft information. Compared to hard-GRAND, pseudo-soft ORBGRAND introduces up to 10dB SNR gains for a target 10^-3 block-error rate.
Cite
@article{arxiv.2207.10842,
title = {GRAND for Fading Channels using Pseudo-soft Information},
author = {Hadi Sarieddeen and Muriel Médard and Ken. R. Duffy},
journal= {arXiv preprint arXiv:2207.10842},
year = {2023}
}
Comments
To appear in the IEEE GLOBECOM 2022 proceedings. arXiv admin note: text overlap with arXiv:2207.10836