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A tradeoff resource allocation based on MF-TDMA scheme in the multibeam data relay satellite systems

机译:多波束数据中继卫星系统中基于MF-TDMA方案的折衷资源分配

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A tremendous increase in the number of distributed satellite constellations with the unscheduled burst data traffic will impose addition and diverse requirements on the DRS (data relay satellite) systems, which increases the complexity for beam management and affects a real-time data return and acquisition. In this paper, we suggested that a large capacity can be achieved by a multibeam DRS system based on multifrequency time division multiple access scheme providing multiaccess for the distributed satellite constellations. Because the space-based information network is characterized by the limited on-board resources, a highly dynamic topology and time-varying intersatellite links, we designed a 2-stage dynamic optimization approach to separate the multiobjective optimization for frequency/time blocks and power, aiming at the rapidly converging to the optimal solution and at the same time meeting the fairness resource allocation. In particular, a capacity-fairness tradeoff algorithm is proposed based on hybrid the enhanced genetic algorithm and the particle swarm optimization. Simulation results show that the tradeoff between maximizing total capacity and providing proportional fairness allocation is well balanced. The proposed algorithm can rapidly converge to adapt to the highly dynamic topology in data relay satellite systems.
机译:计划外的突发数据流量会大大增加分布式卫星星座的数量,这将对DRS(数据中继卫星)系统造成额外的要求和多样化的要求,这会增加波束管理的复杂性并影响实时数据的返回和获取。在本文中,我们建议通过基于多频时分多址方案的多波束DRS系统可以实现大容量,该系统为分布式卫星星座提供多址。由于空基信息网络的特点是机载资源有限,高度动态的拓扑结构和时变的星际链接,因此我们设计了一种两阶段动态优化方法,以分离频率/时间块和功率的多目标优化,旨在迅速收敛到最佳解决方案,同时满足公平资源分配的要求。提出了一种基于混合遗传算法和粒子群算法的容量公平性折衷算法。仿真结果表明,在最大化总容量和提供比例公平分配之间的权衡是平衡的。所提出的算法可以快速收敛以适应数据中继卫星系统中的高动态拓扑。

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