# DSRDM: Digital Signal Recovery Diffusion Model for Semantic Communications

arXiv: https://arxiv.org/abs/2605.27730 · HTML: https://mlsanity.com/paper/2605.27730/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 26 May 2026.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 9 of 9)

- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communications](https://mlsanity.com/paper/2309.08895/) (2023)
- [Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints](https://mlsanity.com/paper/2407.18468/) (2024)
- [Diffusion models for audio semantic communication](https://mlsanity.com/paper/2309.07195/) (2023)
- [DMCE: Diffusion Model Channel Enhancer for Multi-User Semantic Communication Systems](https://mlsanity.com/paper/2401.16017/) (2024)
- [SCDM: Score-Based Channel Denoising Model for Digital Semantic Communications](https://mlsanity.com/paper/2501.17876/) (2025)
- [Goal-Oriented Semantic Communication for Wireless Video Transmission via Generative AI](https://mlsanity.com/paper/2502.20927/) (2025)
- [Image Generation with Supervised Selection Based on Multimodal Features for Semantic Communications](https://mlsanity.com/paper/2411.17428/) (2024)
- [Semantic Communications for Digital Signals via Carrier Images](https://mlsanity.com/paper/2412.07173/) (2024)

## Abstract (arXiv)

Diffusion model (DM) has recently appeared as a promising type of generative model for AI-generated content, which has been widely used for image reconstruction, generation, and channel denoising in semantic communication (SemCom) due to its strong generation capacity. However, most of existing works regarding SemCom remain confined to the image or text transmission, and neglect the commonly adopted digital signals in wireless systems. In this letter, in order to address this gap, we propose and investigate a digital signal recovery diffusion model (DSRDM) for SemCom. Specifically, DSRDM encodes digital signals by gradually adding Gaussian signals to images in the forward diffusion process of DM. After the encoded Gaussian signals embedded in the carrier image are sent to the receiver, it recovers the digital signals by predicting the added Gaussian signals iteratively in the reverse diffusion process. Moreover, to reduce the computation complexity of DSRDM, a signal adding approach is designed to avoid the retraining latency. In particular, we use the latent representation of images instead of themselves as the carrier for digital signals in DSRDM to reduce the inference latency.