用变分扩散模型降低信道解码的计算开销，兼顾高纠错性能。
Variational Diffusion Channel Decoder
- 融合领域特定的信念传播与扩散模型，提升解码效率。
- 相比现有神经解码器，计算成本和模型尺寸显著降低。
- 适合对资源敏感的实时通信与存储系统部署。
神经信道解码作为一种数据驱动的解码策略，在纠错能力上展现出远超传统方法的潜力。然而，基于深度学习的解码器往往带来巨大的模型存储与计算复杂度，限制了其在实际时间敏感、资源受限通信与存储系统中的应用。为此，我们提出一种高效的变分扩散模型信道解码器，将领域特定的信念传播过程与现代扩散模型有机结合。通过利用信念传播的低成本优势与扩散模型的强大学习能力，所提神经解码器在实现极低开销的同时保持优异的纠错性能。实验结果表明，相较于当前最优的神经信道解码器，该模型在显著降低计算成本和模型规模的前提下，实现了最佳的解码性能，为实际部署提供了可行方案。
原文摘要 · Abstract (English)
Neural channel decoder, as a data-driven channel decoding strategy, has shown very promising improvement on error-correcting capability over the classical methods. However, the success of those deep learning-based decoder comes at the cost of drastically increased model storage and computational complexity, hindering their practical adoptions in real-world time-sensitive resource-sensitive communication and storage systems. To address this challenge, we propose an efficient variational diffusion model-based channel decoder, which effectively integrates the domain-specific belief propagation process to the modern diffusion model. By reaping the low-cost benefits of belief propagation and strong learning capability of diffusion model, our proposed neural decoder simultaneously achieves very low cost and high error-correcting performance. Experimental results show that, compared with the state-of-the-art neural channel decoders, our model provides a feasible solution for practical deployment via achieving the best decoding performance with significantly reduced computational cost and model size.
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