From Generation to Restoration: Residual Diffusion for Neural Channel Decoding

Neural decoders have shown strong potential for error correction in short- and moderate-length regimes, yet a fundamental tension remains between decoding accuracy and inference latency. Recent attempts to leverage diffusion probabilistic models for channel decoding typically adopt a fully generative paradigm, initializing the reverse process from an observation-agnostic prior, which overlooks a key structural property of channel decoding: the received signal already contains substantial information about the target codeword. In this work, we propose Channel Residual Diffusion Model (ChRes-DM), a principled diffusion-based decoding framework that reinterprets channel decoding as a directed restoration process anchored at the noisy observation. Instead of sampling from a generic prior, ChRes-DM constructs a geometric residual bridge between the received signal and the clean codeword, explicitly modeling the conditional transport induced by the channel and leading to a deterministic Probability Flow Ordinary Differential Equation (PF-ODE) that governs the decoding dynamics. By eliminating redundant stochastic sampling inherent in conventional diffusion models, ChRes-DM enables efficient iterative decoding with flexible step-skipping, offering fine-grained control over the accuracy–latency trade-off. Extensive experiments across representative channel coding benchmarks demonstrate that ChRes-DM achieves competitive or superior decoding performance compared to existing neural decoders while significantly reducing inference iterations, highlighting diffusion-based residual transport as a promising and scalable paradigm for neural channel decoding.