# DiffCom: Channel Received Signal is a Natural Condition to Guide Diffusion Posterior Sampling

arXiv: https://arxiv.org/abs/2406.07390 · HTML: https://mlsanity.com/paper/2406.07390/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2024.

## Built on by (most cited first, 11 of 11)

- [Semantics-Guided Diffusion for Deep Joint Source-Channel Coding in Wireless Image Transmission](https://mlsanity.com/paper/2501.01138/) (2025)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [ICDM: Interference Cancellation Diffusion Models for Wireless Semantic Communications](https://mlsanity.com/paper/2505.19983/) (2025)
- [SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks](https://mlsanity.com/paper/2511.01466/) (2025)
- [Generative AI Meets 6G and Beyond: Diffusion Models for Semantic Communications](https://mlsanity.com/paper/2511.08416/) (2025)
- [DiT-JSCC: Rethinking Deep JSCC with Diffusion Transformers and Semantic Representations](https://mlsanity.com/paper/2601.03112/) (2026)
- [Low-Latency Generative Semantic Communication via Channel-Realization Flow Matching](https://mlsanity.com/paper/2607.24876/) (2026)
- [Ada-TokenCom: Rate-Adaptive Token Communications via Large-Model-Driven Token Compression and Generation](https://mlsanity.com/paper/2608.28086/) (2026)
- [Generative Semantic Communication via Alternating Dual-Domain Posterior Sampling](https://mlsanity.com/paper/2604.16796/) (2026)
- [JSCGC: Joint Source-Channel-Generation Coding for Wireless Generative Communications](https://mlsanity.com/paper/2606.12858/) (2026)
- [Beyond Token Accuracy: Prioritizing What Matters for Visual Reconstruction](https://mlsanity.com/paper/2610.03822/) (2026)

## Abstract (arXiv)

End-to-end visual communication systems typically optimize a trade-off between channel bandwidth costs and signal-level distortion metrics. However, under challenging physical conditions, this traditional coding and transmission paradigm often results in unrealistic reconstructions with perceptible blurring and aliasing artifacts, despite the inclusion of perceptual or adversarial losses for optimizing. This issue primarily stems from the receiver's limited knowledge about the underlying data manifold and the use of deterministic decoding mechanisms. To address these limitations, this paper introduces DiffCom, a novel end-to-end generative communication paradigm that utilizes off-the-shelf generative priors and probabilistic diffusion models for decoding, thereby improving perceptual quality without heavily relying on bandwidth costs and received signal quality. Unlike traditional systems that rely on deterministic decoders optimized solely for distortion metrics, our DiffCom leverages raw channel-received signal as a fine-grained condition to guide stochastic posterior sampling. Our approach ensures that reconstructions remain on the manifold of real data with a novel confirming constraint, enhancing the robustness and reliability of the generated outcomes. Furthermore, DiffCom incorporates a blind posterior sampling technique to address scenarios with unknown forward transmission characteristics. Extensive experimental validations demonstrate that DiffCom not only produces realistic reconstructions with details faithful to the original data but also achieves superior robustness against diverse wireless transmission degradations. Collectively, these advancements establish DiffCom as a new benchmark in designing generative communication systems that offer enhanced robustness and generalization superiorities.