基于多智能体深度强化学习的智能卫星切换策略OA

The development of the maritime Internet of things(IoT)imposes higher demands on the com-munication quality of ultra-dense low Earth orbit(LEO)satellite networks.Traditional fixed-rule hando-ver strategies struggle to adapt to the high-speed motion of LEO satellites,often leading to frequent han-dovers.This study,referencing the first phase of the Starlink project for constellation modeling,proposes an intelligent handover strategy based on multi-agent deep reinforcement learning.A hierarchical decision-making architecture is constructed to combine traditional threshold-based decision-making with reinforcement learning optimization.Leveraging a multi-terminal collaboration mechanism,it achieves global resource coordination.Multi-dimensional metrics,including carrier-to-noise ratio,elevation angle,and remaining service time,are comprehensively considered to design a comprehensive reward function that guides the agents to learn the optimal handover strategy.Simulation results demonstrate that in an ultra-dense constellation environment,this strategy achieves an average access success rate of 92.4%,an improvement of over 13%compared to traditional methods.Furthermore,it maintains stable performance of 77.6%even under high-load scenarios.Although the handover frequency increases,the continuity of communication is effectively ensured through primary handovers.This provides a novel solu-tion to address the intermittent connectivity issues in the maritime IoT.