Scalable Syndrome-based Neural Decoders for Bit-Interleaved Coded Modulations
<|reference_start|>Scalable Syndrome-based Neural Decoders for Bit-Interleaved Coded Modulations: 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.<|reference_end|>
@article{rovella2024scalable,
    title={Scalable Syndrome-based Neural Decoders for Bit-Interleaved Coded
  Modulations},
    author={Gast'on De Boni Rovella, Meryem Benammar, Tarik Benaddi, Hugo Meric},
    journal={arXiv preprint arXiv:2403.02850},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02850},
    primaryClass={cs.IT math.IT}
}