Scalable Syndrome-based Neural Decoders for Bit-Interleaved Coded Modulations

Fuente: arXiv
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Auteurs principaux: Rovella, Gastón De Boni, Benammar, Meryem, Benaddi, Tarik, Meric, Hugo
Format: Preprint
Publié: 2024
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author Rovella, Gastón De Boni
Benammar, Meryem
Benaddi, Tarik
Meric, Hugo
author_facet Rovella, Gastón De Boni
Benammar, Meryem
Benaddi, Tarik
Meric, Hugo
contents In this work, we introduce a framework that enables the use of Syndrome-Based Neural Decoders (SBND) for high-order Bit-Interleaved Coded Modulations (BICM). To this end, we extend the previous results on SBND, for which the validity is limited to Binary Phase-Shift Keying (BPSK), by means of a theoretical channel modeling of the bit Log-Likelihood Ratio (bit-LLR) induced outputs. We implement the proposed SBND system for two polar codes $(64,32)$ and $(128,64)$, using a Recurrent Neural Network (RNN) and a Transformer-based architecture. Both implementations are compared in Bit Error Rate (BER) performance and computational complexity.
format Preprint
id arxiv_https___arxiv_org_abs_2403_02850
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Scalable Syndrome-based Neural Decoders for Bit-Interleaved Coded Modulations
Rovella, Gastón De Boni
Benammar, Meryem
Benaddi, Tarik
Meric, Hugo
Information Theory
In this work, we introduce a framework that enables the use of Syndrome-Based Neural Decoders (SBND) for high-order Bit-Interleaved Coded Modulations (BICM). To this end, we extend the previous results on SBND, for which the validity is limited to Binary Phase-Shift Keying (BPSK), by means of a theoretical channel modeling of the bit Log-Likelihood Ratio (bit-LLR) induced outputs. We implement the proposed SBND system for two polar codes $(64,32)$ and $(128,64)$, using a Recurrent Neural Network (RNN) and a Transformer-based architecture. Both implementations are compared in Bit Error Rate (BER) performance and computational complexity.
title Scalable Syndrome-based Neural Decoders for Bit-Interleaved Coded Modulations
topic Information Theory
url https://arxiv.org/abs/2403.02850