Variational Diffusion Channel Decoder

TL;DR

This paper introduces an efficient variational diffusion model-based neural channel decoder that combines belief propagation with diffusion models to achieve high error correction with low computational cost and model size.

Contribution

It presents a novel neural decoder integrating belief propagation and diffusion models, significantly reducing complexity while maintaining high decoding performance.

Findings

Achieves the best decoding performance among neural decoders.

Reduces computational cost and model size substantially.

Demonstrates practical feasibility for real-world systems.

Abstract

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.