Scalable Syndrome-based Neural Decoders for Bit-Interleaved Coded Modulations
Abstract
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 and , using a Recurrent Neural Network (RNN) and a Transformer-based architecture. Both implementations are compared in Bit Error Rate (BER) performance and computational complexity.
Keywords
Cite
@article{arxiv.2403.02850,
title = {Scalable Syndrome-based Neural Decoders for Bit-Interleaved Coded Modulations},
author = {Gastón De Boni Rovella and Meryem Benammar and Tarik Benaddi and Hugo Meric},
journal= {arXiv preprint arXiv:2403.02850},
year = {2024}
}
Comments
6 pages, 7 figures. To be published in Proc. IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN 2024), Stockholm, Sweden, May 5-8, 2024. \copyright 2024 IEEE