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 $(64,32)$ 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.

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. ©2024 IEEE