Work

Iterative Error Decimation for Syndrome-Based Neural Network Decoders

Year: 2021

Type: article

Abstract: In this letter, we introduce a new syndrome-based decoder where a deep neural network (DNN) estimates the error pattern from the reliability and syndrome of the received vector. The proposed algorithm works by iteratively selecting the most confident positions to be the error bits of the error pattern, updating the vector received when a new position of the error pattern is selected. Simulation results for the (63,45) and (63,36) BCH codes show that the proposed approach outperforms existing neural network decoders. In addition, the new decoder is flexible in that it can be applied on top of any existing syndrome-based DNN decoder without retraining.

Source: Journal of Communication and Information Systems

DOI: 10.14209/jcis.2021.16

Authors: Jorge Kamassury, Danilo Silva

Institution: Universidade Federal de Santa Catarina

Language: English

Topic: Error Correcting Code Techniques

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Conselho Nacional de Desenvolvimento Científico e Tecnológico

Awards: G2167127580, G6078965650