Iterative Error Decimation for Syndrome-Based Neural Network Decoders

Authors

DOI:

https://doi.org/10.14209/jcis.2021.16

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.

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Published

2021-08-27

How to Cite

Kamassury, J. K. S., & Silva, D. (2021). Iterative Error Decimation for Syndrome-Based Neural Network Decoders. Journal of Communication and Information Systems, 36(1), 151–155. https://doi.org/10.14209/jcis.2021.16

Issue

Vol. 36 No. 1 (2021)

Section

Letters

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Received 2021-02-10
Accepted 2021-07-26
Published 2021-08-27