# EnvCDiff: Joint Refinement of Environmental Information and Channel Fingerprints via Conditional Generative Diffusion Model

arXiv: https://arxiv.org/abs/2505.07894 · HTML: https://mlsanity.com/paper/2505.07894/

## Key facts

- Cited by 1 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 12 of its 18 references that are in the map.
- First posted on arXiv 12 May 2025.

## Placed in

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

## Builds on (most cited first, 12 of 12)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G](https://mlsanity.com/paper/2309.07460/) (2023)
- [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023)
- [How Much Data is Needed for Channel Knowledge Map Construction?](https://mlsanity.com/paper/2312.06966/) (2023)
- [Radiomap Inpainting for Restricted Areas based on Propagation Priority and Depth Map](https://mlsanity.com/paper/2305.15526/) (2023)
- [An I2I Inpainting Approach for Efficient Channel Knowledge Map Construction](https://mlsanity.com/paper/2406.09822/) (2024)
- [Adaptive Bayesian Radio Tomography](https://mlsanity.com/paper/1804.02084/) (2018)
- [GDM4MMIMO: Generative Diffusion Models for Massive MIMO Communications](https://mlsanity.com/paper/2412.18281/) (2024)
- [Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information](https://mlsanity.com/paper/2505.06900/) (2025)

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

- [Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework](https://mlsanity.com/paper/2604.27574/) (2026)

## Abstract (arXiv)

The paradigm shift from environment-unaware communication to intelligent environment-aware communication is expected to facilitate the acquisition of channel state information for future wireless communications. Channel Fingerprint (CF), as an emerging enabling technology for environment-aware communication, provides channel-related knowledge for potential locations within the target communication area. However, due to the limited availability of practical devices for sensing environmental information and measuring channel-related knowledge, most of the acquired environmental information and CF are coarse-grained, insufficient to guide the design of wireless transmissions. To address this, this paper proposes a deep conditional generative learning approach, namely a customized conditional generative diffusion model (CDiff). The proposed CDiff simultaneously refines environmental information and CF, reconstructing a fine-grained CF that incorporates environmental information, referred to as EnvCF, from its coarse-grained counterpart. Experimental results show that the proposed approach significantly improves the performance of EnvCF construction compared to the baselines.