# DMCE: Diffusion Model Channel Enhancer for Multi-User Semantic Communication Systems

arXiv: https://arxiv.org/abs/2401.16017 · HTML: https://mlsanity.com/paper/2401.16017/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## 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)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Generative Semantic Communication: Diffusion Models Beyond Bit Recovery](https://mlsanity.com/paper/2306.04321/) (2023)
- [CDDM: Channel Denoising Diffusion Models for Wireless Communications](https://mlsanity.com/paper/2305.09161/) (2023)
- [Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling](https://mlsanity.com/paper/2308.05583/) (2023)
- [Energy Efficient Semantic Communication over Wireless Networks with Rate Splitting](https://mlsanity.com/paper/2301.01987/) (2023)
- [Toward 6G Native-AI Network: Foundation Model based Cloud-Edge-End Collaboration Framework](https://mlsanity.com/paper/2310.17471/) (2023)
- [Wireless Transmission of Images With The Assistance of Multi-level Semantic Information](https://mlsanity.com/paper/2202.04754/) (2022)
- [Image Segmentation Semantic Communication over Internet of Vehicles](https://mlsanity.com/paper/2210.05321/) (2022)
- [Code-Division OFDM Joint Communication and Sensing System for 6G Machine-type Communication](https://mlsanity.com/paper/2304.01517/) (2023)

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

- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [Language-oriented Semantic Communication for Image Transmission with Fine-Tuned Diffusion Model](https://mlsanity.com/paper/2409.17104/) (2024)
- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [Generative Semantic Communication: Architectures, Technologies, and Applications](https://mlsanity.com/paper/2412.08642/) (2024)
- [Training-Free Multi-User Generative Semantic Communications via Null-Space Diffusion Sampling](https://mlsanity.com/paper/2405.09866/) (2024)
- [A Survey on Semantic Communications in Internet of Vehicles](https://mlsanity.com/paper/2503.03767/) (2025)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [Enhancements in Score-based Channel Estimation for Real-Time Wireless Systems](https://mlsanity.com/paper/2509.07839/) (2025)
- [Wireless Video Semantic Communication with Decoupled Diffusion Multi-frame Compensation](https://mlsanity.com/paper/2511.02478/) (2025)
- [Conditional Denoising Diffusion Autoencoders for Wireless Semantic Communications](https://mlsanity.com/paper/2509.22282/) (2025)
- [DSRDM: Digital Signal Recovery Diffusion Model for Semantic Communications](https://mlsanity.com/paper/2605.27730/) (2026)

## Abstract (arXiv)

To achieve continuous massive data transmission with significantly reduced data payload, the users can adopt semantic communication techniques to compress the redundant information by transmitting semantic features instead. However, current works on semantic communication mainly focus on high compression ratio, neglecting the wireless channel effects including dynamic distortion and multi-user interference, which significantly limit the fidelity of semantic communication. To address this, this paper proposes a diffusion model (DM)-based channel enhancer (DMCE) for improving the performance of multi-user semantic communication, with the DM learning the particular data distribution of channel effects on the transmitted semantic features. In the considered system model, multiple users (such as road cameras) transmit semantic features of multi-source data to a receiver by applying the joint source-channel coding (JSCC) techniques, and the receiver fuses the semantic features from multiple users to complete specific tasks. Then, we propose DMCE to enhance the channel state information (CSI) estimation for improving the restoration of the received semantic features. Finally, the fusion results at the receiver are significantly enhanced, demonstrating a robust performance even under low signal-to-noise ratio (SNR) regimes, enabling the generation of effective object segmentation images. Extensive simulation results with a traffic scenario dataset show that the proposed scheme can improve the mean Intersection over Union (mIoU) by more than 25% at low SNR regimes, compared with the benchmark schemes.