A tradeoff resource allocation based on MF‐TDMA scheme in the multibeam data relay satellite systems. (3rd September 2018) - Wang, Xingyu; Li, Yongjun; Zhao, Shanghong; Zheng, Yongxing; Zhu, Zhuodan; Cao, Guixing
Summary: 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. Abstract : A multibeam data relay system based on multifrequency time division multiple access scheme provides multiaccess for the distributed satellite constellations in the space‐based information network. Because the space‐based information network is characterized by the limited on‐board resources and a highly dynamic topology, 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 meeting the fairness resource allocation. Simulation results show that the tradeoff between maximizing total capacity and providing proportional fairness allocation is well balanced.