A Graph Reinforcement Learning-Based Handover Strategy for Low Earth Orbit Satellites under Power Grid Scenarios

Abstract

Amidst the escalating need for stable power supplies and high-quality communication services in remote regions globally, due to challenges associated with deploying a conventional power communication infrastructure and its susceptibility to natural disasters, LEO satellite networks present a promising solution for broad geographical coverage and the provision of stable and high-speed communication services in remote regions. Given the necessity for frequent handovers to maintain service continuity, due to the high mobility of LEO satellites, a primary technical challenge confronting LEO satellite networks lies in efficiently managing the handover process between satellites, to guarantee the continuity and quality of communication services, particularly for power services. Thus, there is a critical need to explore satellite handover optimization algorithms. This paper presents a handover optimization scheme that integrates deep reinforcement learning (DRL) and graph neural networks (GNN) to dynamically optimize the satellite handover process and adapt to the time-varying satellite network environment. DRL models can effectively detect changes in the topology of satellite handover graphs across different time periods by leveraging the powerful representational capabilities of GNNs to make optimal handover decisions. Simulation experiments confirm that the handover strategy based on the fusion of message-passing neural network and deep Q-network algorithm (MPNN-DQN) outperforms traditional handover mechanisms and DRL-based strategies in reducing handover frequency, lowering communication latency, and achieving network load balancing. Integrating DRL and GNN into the satellite handover mechanism enhances the communication continuity and reliability of power systems in remote areas, while also offering a new direction for the design and optimization of future power system communication networks. This research contributes to the advancement of sophisticated satellite communication architectures that facilitate high-speed and reliable internet access in remote regions worldwide.