Noise Error Pattern Generation Based on Successive Addition-Subtraction for Guessing Decoding

TL;DR

This paper introduces a successive addition-subtraction method for generating noise error patterns in GRAND decoding, aiming to improve decoding efficiency and hardware implementation.

Contribution

It proposes a novel noise error pattern generation scheme based on alternating 1 and 0 bursts, with theoretical validation and potential for future GRAND research.

Findings

The algorithm effectively generates noise error permutations.

Theoretical correctness of the method is demonstrated.

Provides a foundation for GRAND algorithm hardware development.

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.