Noise Error Pattern Generation Based on Successive Addition-Subtraction for Guessing Decoding
Year: 2021
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Zhan, Ming, Pang, Zhibo, Yu, Kan, Xu, Jing, Wu, Fang
Institutions: ABB (Sweden), Southwest University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G3557813679
OpenAlex ID: W3208642024