用结构化自编码器实现中等码长下的不等误差保护，提升通信可靠性。
Structured Superposition of Autoencoders for UEP Codes at Intermediate Blocklengths
- 将自编码器分块设计，构建可扩展的不等误差保护架构。
- 在中等码长下性能超越随机叠加编码的理论极限。
- 适合下一代网络中需要灵活可靠性的场景。
不等误差保护（UEP）编码可在传输信息中提供差异化可靠性，对现代通信系统至关重要。基于自编码器（AE）的编码设计在学习型等误差保护（EEP）方案中展现出潜力，但其在中等码长下的不等误差保护应用仍鲜有探索，主要受限于AE模型复杂度上升。受传统UEP方案中叠加编码与逐次干扰消除（SIC）解码有效性的启发，本文提出一种结构化AE架构，将编码与解码分解为更小的自编码子块，使基于AE的UEP码可扩展至更大码长，同时保持高效训练。该方法提供了灵活调节不同数据段可靠性水平的能力，并适应多种系统参数。数值结果表明，所提方法在中等码长下性能优于基于随机叠加编码与SIC解码的已知可达性界，证明了其在下一代网络中的可扩展性与高效性。
原文摘要 · Abstract (English)
Unequal error protection (UEP) coding that enables differentiated reliability levels within a transmitted message is essential for modern communication systems. Autoencoder (AE)-based code designs have shown promise in the context of learned equal error protection (EEP) coding schemes. However, their application to UEP remains largely unexplored, particularly at intermediate blocklengths, due to the increasing complexity of AE-based models. Inspired by the proven effectiveness of superposition coding and successive interference cancellation (SIC) decoding in conventional UEP schemes, we propose a structured AE-based architecture that extends AE-based UEP codes to substantially larger blocklengths while maintaining efficient training. By structuring encoding and decoding into smaller AE subblocks, our method provides a flexible framework for fine-tuning UEP reliability levels while adapting to diverse system parameters. Numerical results show that the proposed approach improves over established achievability bounds of randomized superposition coding-based UEP schemes with SIC decoding, making the proposed structured AE-based UEP codes a scalable and efficient solution for next-generation networks.
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