Joint Beam Management and Resource Allocation in a GEO and LEO Spectrum-Sharing System for Effective Interference Avoidance

The large-scale expansion of low Earth orbit (LEO) satellite constellations has ushered in a new era of space communications. However, the limitations of orbital and spectrum resources pose significant obstacles. With the aid of cognitive radio technology, spectrum sharing between LEO and geostationary Earth orbit (GEO) satellite communication systems provides a feasible solution to alleviate spectrum scarcity. Aiming at the scenario where LEO satellites pass through GEO satellite coverage areas, we seek to explore flexible and efficient spectrum sharing schemes to achieve co-linear interference avoidance. In this paper, we formulate a joint optimization problem for beam management and resource allocation, emphasizing the necessity of improving system throughput while ensuring quality of service for GEO satellite users. To address this issue, we first construct a space information model to describe the signal connections between satellites and users, as well as the overlapping coverage relationships among satellites. Then, we develop an adaptive approach that integrates both model-based and model-free deep reinforcement learning (DRL) methods. Guided by the space information graph, the model-based DRL employs a hybrid attention mechanism to mitigate beam-level interference through beam hopping and pointing optimization. Meanwhile, the model-free DRL optimizes channel allocation and power control to achieve user-level spectrum sharing, aiming to maximize resource utilization through continuous interaction. Simulation results demonstrate that the proposed algorithm significantly enhances the overall system throughput, showing its potential for effective interference coordination in the coexistence of LEO and GEO satellite communication systems.