Noise Error Pattern Generation Based on Successive Addition-Subtraction for Guessing Decoding
Information Theory
2021-11-02 v1 math.IT
Abstract
Guessing random additive noise decoding (GRAND) algorithm has emerged as an excellent decoding strategy that can meet both the high reliability and low latency constraints. This paper proposes a successive addition-subtraction algorithm to generate noise error permutations. A noise error patterns generation scheme is presented by embedding the "1" and "0" bursts alternately. Then detailed procedures of the proposed algorithm are presented, and its correctness is also demonstrated through theoretical derivations. The aim of this work is to provide a preliminary paradigm and reference for future research on GRAND algorithm and hardware implementation.
Cite
@article{arxiv.2111.00695,
title = {Noise Error Pattern Generation Based on Successive Addition-Subtraction for Guessing Decoding},
author = {Ming Zhan and Zhibo Pang and Kan Yu and Jing Xu and Fang Wu},
journal= {arXiv preprint arXiv:2111.00695},
year = {2021}
}
Comments
6 pages, 7 figures, submitted to IEEE Communications Letters