# Conditional Denoising Diffusion for ISAC Enhanced Channel Estimation in Cell-Free 6G

arXiv: https://arxiv.org/abs/2506.06942 · HTML: https://mlsanity.com/paper/2506.06942/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective](https://mlsanity.com/paper/2310.01036/) (2023)
- [Integrated Sensing and Communication Signals Toward 5G-A and 6G: A Survey](https://mlsanity.com/paper/2301.03857/) (2023)
- [Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks](https://mlsanity.com/paper/2502.12622/) (2025)
- [A Survey on User-Centric Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2104.13667/) (2021)
- [Cell-Free ISAC MIMO Systems: Joint Sensing and Communication Beamforming](https://mlsanity.com/paper/2301.11328/) (2023)
- [Generative AI Empowered LiDAR Point Cloud Generation with Multimodal Transformer](https://mlsanity.com/paper/2406.18542/) (2024)
- [Generative AI-enabled Blockage Prediction for Robust Dual-Band mmWave Communication](https://mlsanity.com/paper/2501.11763/) (2025)
- [Power Allocation for Joint Communication and Sensing in Cell-Free Massive MIMO](https://mlsanity.com/paper/2209.01864/) (2022)
- [Cell-Free Integrated Sensing and Communication: Principles, Advances, and Future Directions](https://mlsanity.com/paper/2502.20345/) (2025)

## Built on by (most cited first, 1 of 1)

- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)

## Abstract (arXiv)

Cell-free Integrated Sensing and Communication (ISAC) aims to revolutionize 6th Generation (6G) networks. By combining distributed access points with ISAC capabilities, it boosts spectral efficiency, situational awareness, and communication reliability. Channel estimation is a critical step in cell-free ISAC systems to ensure reliable communication, but its performance is usually limited by challenges such as pilot contamination and noisy channel estimates. This paper presents a novel framework leveraging sensing information as a key input within a Conditional Denoising Diffusion Model (CDDM). In this framework, we integrate CDDM with a Multimodal Transformer (MMT) to enhance channel estimation in ISAC-enabled cell-free systems. The MMT encoder effectively captures inter-modal relationships between sensing and location data, enabling the CDDM to iteratively denoise and refine channel estimates. Simulation results demonstrate that the proposed approach achieves significant performance gains. As compared with Least Squares (LS) and Minimum Mean Squared Error (MMSE) estimators, the proposed model achieves normalized mean squared error (NMSE) improvements of 8 dB and 9 dB, respectively. Moreover, we achieve a 27.8% NMSE improvement compared to the traditional denoising diffusion model (TDDM), which does not incorporate sensing channel information. Additionally, the model exhibits higher robustness against pilot contamination and maintains high accuracy under challenging conditions, such as low signal-to-noise ratios (SNRs). According to the simulation results, the model performs well for users near sensing targets by leveraging the correlation between sensing and communication channels.