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

TL;DR

This paper introduces EnvCDiff, a deep generative diffusion model that jointly refines environmental data and channel fingerprints to produce detailed, environment-aware channel information for improved wireless communication design.

Contribution

It presents a novel conditional generative diffusion model (CDiff) that enhances coarse environmental and channel fingerprint data into fine-grained, environment-aware information.

Findings

Significant improvement in EnvCF accuracy over baselines

Effective reconstruction of fine-grained environmental and channel data

Enhanced environment-aware communication capabilities

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.