Variational Diffusion Channel Decoder
For communication and storage systems, this work addresses the practical deployment challenge of neural decoders by balancing performance and efficiency.
The paper proposes a variational diffusion model-based channel decoder that integrates belief propagation to achieve low cost and high error-correcting performance, outperforming state-of-the-art neural decoders with reduced computational cost and model size.
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.