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

2025-05-12

AI Summary

To address the prevalent coarse-grained environmental information and channel fingerprints (CFs) in wireless communications—which hinder high-precision transmission design—this paper proposes an environment-aware fine-grained channel fingerprint (EnvCF) reconstruction method. We introduce conditional denoising diffusion models (CDiff) for the first time to enable cross-modal collaborative refinement, achieving implicit coupling and joint enhancement of environmental context and CFs through multimodal feature alignment, optimized noise scheduling, and end-to-end joint training. Experimental results demonstrate that the proposed method achieves an average 37.2% improvement over baselines in key metrics—including localization error and channel prediction mean squared error—enabling sub-meter-accurate environment-aware communication design.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multimodal Learning
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Content-based information diffusion

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.

Problem

• Refining coarse-grained environmental information for wireless communications
• Enhancing Channel Fingerprint accuracy using conditional generative models
• Joint reconstruction of environment-aware fine-grained Channel Fingerprints

Innovation

• Uses conditional generative diffusion model
• Refines environmental and channel fingerprints jointly
• Reconstructs fine-grained EnvCF from coarse data