Modal-Based Multi-Scatterer Channel Model for Localized Radiomap Extrapolation

TL;DR

This paper introduces a physically interpretable, spherical wave mode expansion-based channel model for multiple scatterers, enabling accurate radiomap extrapolation from sparse measurements in high-frequency wireless environments.

Contribution

It develops a novel spherical wave mode expansion model for multiple scatterers and formulates an inverse optimization approach to learn scattering responses and locations from sparse data.

Findings

Accurately reconstructs and extrapolates radiomaps in spatial and beam domains.

Employs a simplified low-order mode model for dense scatterer environments.

Demonstrates effectiveness through simulation results.

Abstract

A radiomap, representing the spatial distribution of wireless signal strength within a specific region, is fundamentally determined by the local propagation channel and finds extensive applications in network planning and optimization. The channel model is inherently linked to electromagnetic (EM) wave propagation, and the advent of high-frequency communications presents a new picture - microscopic (and thus negligible) scatterers in lower frequency bands become mesoscopic, rendering non-negligible EM effects. In this paper, we establish a channel model for multiple scatterers based on a spherical wave mode expansion. The source radiation, single scatterer response and multiple scatterer interactions are formed in the superposition of spherical-wave modes, capturing the multi-path effect in wave perspective. Iterative methods are used to handle the massive coupling between scatterers. This forward model is converted to an inverse optimization problem, where the scattering responses and the scatterer locations are jointly learned from sparse field measurements. A simplified approximate model is then introduced, employing fewer and simpler low-order modes while still allowing a larger number of more densely placed scatterers. Simulation results demonstrate that the proposed model accurately reconstructs and extrapolates radiomaps in both the spatial domain and the beam domain. Overall, the proposed framework offers a physically interpretable approach to localized propagation modeling.