Maximizing User Admittance for Cognitive Satellite-Terrestrial Networks Using ODE-Inspired Spectral Radius Estimation

Cognitive satellite-terrestrial networks (CSTNs) are a promising technology that optimize satellite efficiency and coverage. In this paper, we present a novel QoS-aware, deep learning (DL) algorithm that integrates with space-air-ground channels and exploits beam utilization by maximizing user admittance. To this end, we characterize the spectral radius as a critical concept that is indicative of resource utilization in the multibeam cluster. Our findings show that a smart use of the spectral radius, learned with ordinary differential equation networks (ODE-Nets), could maximize user admittance and outperform baselines like overlay and underlay, and predict the optimal power allocation vector.