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为满足第六代移动通信(6G)的全球覆盖和高速率通信要求，已出现或将成形多个大规模低地球轨道(Low Earth Orbit, LEO)星座。而LEO极轨星座在中高纬度区域均存在严重的重叠覆盖问题，导致通信资源的浪费及系统间同频干扰。通过波束关闭(Beam Switch Off, BSO)策略关闭部分波束的方式，可以在减少波束重叠覆盖的同时节约卫星能量。针对同频干扰问题，需要对关闭波束的选择进行优化，提高产生干扰波束的被关闭权重。提出了一种使用深度强化学习(Deep Reinforcement Learning, DRL)进行参数优化的贪婪策略用于解决该问题。在搭载真实天线模型的铱星星座场景下，该算法关闭了37.4%的波束，将系统等效功率通量密度(Equivalent Power Flux Density, EPFD)降低至国际电信联盟(International Telecommunication Union, ITU)的限值要求，确保在98%的时间内在20 kHz的参考带宽下不超过-194 dB(W/m2),在进行BSO的同时有效减缓了系统间干扰。
Abstract：In order to meet the requirements for 6G global coverage and high-rate communications, several large-scale Low Earth Orbit(LEO) satellite constellations have emerged or will take shape. Among them, LEO polar orbit constellations have serious overlapping coverage problems in middle and high latitude regions. This leads to the waste of communication resources and inter-system co-channel interference. By implementing the Beam Switch Off(BSO) strategy to turn off certain beams, it is possible to reduce the overlapping coverage of beams and save satellite energy as well. Regarding the co-frequency interference, it is necessary to optimize the selection of beams to be deactivated, and increase the weight of the interfering beams to be deactivated. A Deep Reinforcement Learning(DRL) enhanced greedy strategy is proposed for this optimization problem. In the Iridium Next constellation scenario, a realistic antenna model is used. Simulation results show that the algorithm effectively deactivates 37.4% of the beams and reduces the system's Equivalent Power Flux Density(EPFD) to not more than-194 dB(W/m2) in 20 kHz reference bandwidth for at least 98% of the time, which meets the limit requirements of the International Telecommunication Union(ITU). While implementing BSO, the inter-system interference is effectively mitigated.
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基本信息:
DOI：
中图分类号:TP18;TN975;TN927.2
引用信息:
[1]袁书豪,尹良.基于改进贪婪策略的NGSO干扰减缓方法[J].无线电工程,2025,55(04):891-897.
基金信息: