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

TL;DR

This work tackles the challenge of building fine-grained EnvCF from coarse-grained measurements by formulating EnvCF as a joint environment-channel fingerprint map. It introduces a customized conditional diffusion model (CDiff) that uses low-resolution EnvCF as side information to perform conditional inversion, optimizing an ELBO-based objective to approximate the conditional data distribution $p(\boldsymbol{\mathsf{F}}_{\mathrm{HR}}|\boldsymbol{\mathsf{F}}_{\mathrm{LR}})$. The proposed approach integrates a forward Gaussian diffusion and a LR-conditioned denoiser to reconstruct high-resolution EnvCF, achieving substantial gains over traditional interpolation and vision-based baselines on the RadioMapSeer dataset, with metrics like PSNR, SSIM, and NMSE demonstrating notable improvements. By enabling finer-grained environment-channel knowledge, the method supports more accurate beamforming, localization, and sensing-driven wireless design in environment-aware 6G/beyond networks.

Abstract

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.

Figures (2)

• Figure 1: Schematic of the proposed CDiff workflow and the architecture of the conditional denoising neural network.
• Figure 2: Random visualizations of the HR EnvCF reconstruction results using the proposed CDiff and baselines.