西安电子科技大学学报（自然科学版）2025，Vol.52Issue(3)：73-84,12.DOI:10.19665/j.issn1001-2400.20241104
基于DWGA的低轨卫星多波束调度策略
Multi-beam scheduling strategy for the low-earth orbit satellite based on DWGA
摘要
Abstract
Low-earth orbit(LEO)satellite communication is an important part of the air-space-ground integrated network and plays an important role in real-time operations and emergency communications.A multi-beam scheduling strategy for LEO satellites based on delay weighting and the genetic algorithm(DWGA)is proposed to solve the problem of low utilization of beam resources due to the large spatial-temporal difference of user service requirements.First,a multi-beam scheduling architecture for LEO satellites is designed.Second,the impact of the co-channel reuse distance on the carrier-to-interference-plus-noise ratio is analyzed by constructing a beam interference model,thereby determining an interference avoidance scheme for full-frequency reuse.Then the real population density distribution and user scheduling rate are used to build the business model.Subsequently,the capacity allocated to different beam cells is determined by weighting based on the maximum tolerable delay of traffic to derive a capacity scheduling factor,thereby preventing traffic data failures.Finally,with the objective of maximizing capacity,a genetic algorithm(GA)is employed to determine the beam-hopping patterns under various scheduling sequences.Simulation results demonstrate that,when compared to the genetic algorithm,the round robin(RR)algorithm,and the random algorithm(RA),the proposed algorithm's capacity utilization rate has increased on average by 2.74％,45.5％,and 46.23％,respectively,and the service timeout failure rates have been reduced on average by 2.52％,20.66％,and 25.36％,respectively.Compared to genetic algorithms,the cost of the proposed algorithm is storage capacity.关键词
低轨卫星/跳波束/资源分配/遗传算法Key words
low earth orbit satellite/beam hopping/resource allocation/genetic algorithms分类
信息技术与安全科学引用本文复制引用
李红光,石晶林,周一青,刘垚圻..基于DWGA的低轨卫星多波束调度策略[J].西安电子科技大学学报（自然科学版）,2025,52(3):73-84,12.基金项目
中国科学院计算所创新课题(E261020) （E261020）
江苏省重点研发计划(BE2021013-2) （BE2021013-2）
