تقرير
Scalable Syndrome-based Neural Decoders for Bit-Interleaved Coded Modulations
| العنوان: | Scalable Syndrome-based Neural Decoders for Bit-Interleaved Coded Modulations |
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| المؤلفون: | Rovella, Gastón De Boni, Benammar, Meryem, Benaddi, Tarik, Meric, Hugo |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Computer Science - Information Theory |
| الوصف: | 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. Comment: 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 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2403.02850 |
| رقم الأكسشن: | edsarx.2403.02850 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |