用深度学习统一解码打孔卷积码和Turbo码，适配多种速率且符合协议要求。
Decoding for Punctured Convolutional and Turbo Codes: A Deep Learning Solution for Protocols Compliance
- 设计感知打孔模式的LSTM解码器，直接融合打孔结构提升适应性。
- 在AWGN与瑞利信道下，误码率显著低于传统方法，性能更稳健。
- 适合需要高兼容性、多速率支持的通信系统研发与部署。
基于神经网络的解码方法在提升纠错性能方面展现出潜力，但对打孔码的处理仍面临挑战，尤其在适应可变码率或满足协议兼容性方面表现不足。本文提出一种统一的基于长短期记忆（LSTM）的神经解码器，用于打孔卷积码与Turbo码。其核心是打孔感知嵌入机制，将打孔模式直接融入神经网络，实现对不同码率的无缝适应。同时，设计了平衡误码率训练策略，确保解码器在不同码长、码率和信道条件下的鲁棒性，从而满足协议兼容性要求。在加性白高斯噪声（AWGN）与瑞利衰落信道中的大量仿真表明，该神经解码器优于传统解码技术，在解码准确性和鲁棒性上均有显著提升。
原文摘要 · Abstract (English)
Neural network-based decoding methods show promise in enhancing error correction performance but face challenges with punctured codes. In particular, existing methods struggle to adapt to variable code rates or meet protocol compatibility requirements. This paper proposes a unified long short-term memory (LSTM)-based neural decoder for punctured convolutional and Turbo codes to address these challenges. The key component of the proposed LSTM-based neural decoder is puncturing-aware embedding, which integrates puncturing patterns directly into the neural network to enable seamless adaptation to different code rates. Moreover, a balanced bit error rate training strategy is designed to ensure the decoder's robustness across various code lengths, rates, and channels. In this way, the protocol compatibility requirement can be realized. Extensive simulations in both additive white Gaussian noise (AWGN) and Rayleigh fading channels demonstrate that the proposed neural decoder outperforms conventional decoding techniques, offering significant improvements in decoding accuracy and robustness.
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