Beam-Aware Radio Map Estimation With Physics-Consistent Parametric Modeling for Unknown Multiple Satellites

2026-05-08

AI Summary

This work addresses the challenges of unknown active satellites, unobservable beam pointing directions, and difficult signal strength calibration in dense low Earth orbit (LEO) satellite constellations by proposing a beam-aware radio channel map estimation framework. The approach integrates physically consistent parametric modeling with data-driven strategies, leveraging structural priors that encode geometric and beamforming relationships, along with adaptive model order selection, to jointly infer the number of active satellites and reconstruct a continuous received signal strength field. Experimental results demonstrate that the proposed method significantly enhances spatial correlation of signal strength, reduces root mean square error, and achieves superior F1 scores across varying signal-to-noise ratios, total satellite counts, and proportions of active satellites.

Abstract

Satellite networks with dense low Earth orbit (LEO) constellations rely on aggressive spectrum reuse, making co-channel interference a dominant and rapidly varying factor that limits link availability and complicates spectrum sharing and compliance. Satellite radio map (RM) construction is therefore essential for interference cognition, yet it is challenging because the active satellite set is unknown, beam footprints and pointing are not directly observable, and received signal strength (RSS) measurements are difficult to calibrate under coupled link budget variations and noise. These latent uncertainties yield a severely underdetermined inverse problem with strong signature coherence, where existing methods often trade detection recall for precision and still fail to recover a faithful continuous RSS field. This paper proposes a beam-aware RM estimation framework that unifies active satellite identification and RSS field reconstruction through physics-consistent parametric modeling. An interpretable structural prior links geometry and beam shaping to spatial RSS formation, and an adaptive model order selection strategy infers the number of active satellites from measurements by balancing fit and complexity. Extensive experiments across varying signal to noise ratio (SNR), total satellite count, and active satellite count demonstrate consistently higher RSS spatial correlation, lower root mean squared error (RMSE), and improved F1 score, validating the proposed approach for interference-aware satellite RM construction in satellite networks.

Problem

• radio map estimation
• co-channel interference
• LEO satellite networks
• received signal strength
• underdetermined inverse problem

Innovation

• beam-aware radio map
• physics-consistent parametric modeling
• active satellite identification
• adaptive model order selection
• co-channel interference