Neural Network Aided Impulsive Perturbation Decoding for Short Raptor-Like LDPC Codes

Autor:	Hyunjae Lee, Young-Sung Kil, Sang-Hyo Kim, Min Young Chung
Rok vydání:	2022
Předmět:	Artificial neural network Control and Systems Engineering Network packet Heuristic (computer science) Computer science Code (cryptography) Electrical and Electronic Engineering Low-density parity-check code Algorithm Symbol (chemistry) Decoding methods 5G Computer Science::Information Theory
Zdroj:	IEEE Wireless Communications Letters. 11:268-272
ISSN:	2162-2345 2162-2337
DOI:	10.1109/lwc.2021.3125578
Popis:	As short packet communications become more important, multi-round belief propagation decoding with impulsive perturbation (MBPD-IP) was proposed to improve the decoding of short low-density parity-check (LDPC) codes. To operate the MBPD-IP effectively, it is crucial to select proper symbols to be perturbed in as few trials as possible. Unfortunately, existing perturbation symbol selection criteria are heuristic and not effective for special codes such as raptor-like (RL) LDPC codes. In this letter, a neural network (NN) based perturbation symbol selection scheme for MBPD-IP is proposed where the symbols to be perturbed are selected from a pre-trained NN. It is shown that the proposed scheme performs significantly better than existing schemes for the RL LDPC code of 5G new radio and still works well for plain code structures.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9cbbe46c00f027e9c55abd2dbb602a63 https://doi.org/10.1109/lwc.2021.3125578 Zobrazit plný text záznamu