Channel Knowledge Map Construction via Physics-Inspired Diffusion Model Without Prior Observations

TL;DR

This paper introduces a physics-inspired diffusion model for constructing accurate channel knowledge maps in large-scale fading scenarios without prior observations, emphasizing physical constraints over traditional vision-based methods.

Contribution

It proposes a novel diffusion model incorporating physical constraints for CKM construction, addressing the limitations of existing vision-based approaches and enabling large-scale, observation-free mapping.

Findings

Outperforms existing methods in construction accuracy

Provides a unified framework for diverse and physically consistent CKM generation

Demonstrates strong potential for large-scale fading scenarios

Abstract

The ability to construct channel knowledge map (CKM) with high precision is essential for environment awareness in 6G wireless systems. However, most existing CKM construction methods formulate the task as an image super-resolution or generation problem, thereby employing models originally developed for computer vision. As a result, the generated CKMs often fail to capture the underlying physical characteristics of wireless propagation. In this paper, considering that acquiring channel observations incurs non-negligible time and cost, we focus on constructing CKM for large-scale fading scenarios without relying on prior observations, and we design three physics-based constraints to characterize the spatial distribution patterns of large-scale fading. By integrating these physical constraints with state-of-the-art diffusion model that possesses superior generative capability, a physics-inspired diffusion model for CKM construction is proposed. Following this motivation, we derive the loss function of the diffusion model augmented with physics-based constraint terms and further design the training and generation framework for the proposed physics-inspired CKM generation diffusion model. Extensive experiments show that our approach outperforms all existing methods in terms of construction accuracy. Moreover, the proposed model provides a unified and effective framework with strong potential for generating diverse, accurate, and physically consistent CKM.