无线电工程2026，Vol.56Issue(4)：625-634,10.DOI:10.3969/j.issn.1003-3106.2026.04.007
基于深度强化学习的低轨卫星跳波束资源分配方法
Deep Reinforcement Learning-based Beam Hopping Resource Allocation Method for Low Earth Orbit Satellite
摘要
Abstract
With the rapid development of digital economy,low earth orbit satellite communication has emerged as a crucial complement to terrestrial networks and an effective means of bridging the digital divide,owing to its wide coverage and relatively low latency compared to medium-and high-orbit satellites.However,the uneven spatial distribution of users,together with the bursty and tidal characteristics of service demands,leads to inefficiencies in conventional static resource allocation methods.To address the mismatch between user demand and resource supply in low earth orbit satellite Beam Hopping(BH)systems,a resource allocation scheme based on Deep Reinforcement Learning(DRL)is proposed.By formulating a joint optimization problem of BH patterns and power allocation,a Deep Q-network(DQN)algorithm integrated with Convolutional Neural Networks(CNN)is designed to achieve joint decision-making of beam switching and discrete power control.Simulation results demonstrate that the proposed method significantly improves system throughput and user satisfaction under various conditions of power constraints,user scales,and the number of active beams,thereby providing an efficient solution for intelligent resource management in LEO satellite communication.关键词
低轨卫星/跳波束/深度强化学习/资源分配Key words
low earth orbit satellite/BH/DRL/resource allocation分类
信息技术与安全科学引用本文复制引用
孙天宇,袁硕,孙耀华,彭木根..基于深度强化学习的低轨卫星跳波束资源分配方法[J].无线电工程,2026,56(4):625-634,10.基金项目
国家自然科学基金(62501069,62371071)National Natural Science Foundation of China(62501069,62371071) （62501069,62371071）
