Neural Network-Like LDPC Decoder for Mobile Applications

Authors

Keywords:

Channel coding, iterative decoding, Low-Density Parity-Check (LDPC) codes, Neural Networks.

Abstract

This paper presents a low complexity iterative decoder for Low-Density Parity-Check (LDPC) codes for mobile applications using a Neural Network-like (NNL) structure and a modified Single-Layer Perceptron (SLP) training algorithm. The proposed approach allows for midrange decoding performance with a minimum gap to Shannon-limit of 3.19 dB at a frame error rate of 10^-4 for the short frame and the code rate 13/15 of the next-generation Digital Terrestrial Television Broadcasting (DTTB) standard of the Advanced Television Systems Committee (ATSC), the "ATSC 3.0". The NNL decoder has a low decoding time, thus, it would be suitable for low power embedded systems, software-defined radio implementation tools, and software-based DTTB receptors.

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Published

2022-12-21

How to Cite

Jerji, F., Silva, L., & Akamine, C. (2022). Neural Network-Like LDPC Decoder for Mobile Applications. SET INTERNATIONAL JOURNAL OF BROADCAST ENGINEERING, 8, 11. Retrieved from https://revistas.set.org.br/ijbe/article/view/246

Issue

Vol. 8 (2022)

Section

Communication, Networking & Broadcasting

License

Copyright (c) 2023 SET INTERNATIONAL JOURNAL OF BROADCAST ENGINEERING

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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