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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. KEYWORDS data relay satellite systems, space-based information networks, multiple beams, resource allocation, satellite communications, proportional fairness 1 | INTRODUCTION Space-based information network (SBIN) with the cooperation scheme of the data relay satellite (DRS) systems have a significant role of achieving a real-time data return and acquisition for mission satellites in the low-earth orbit (LEO) or medium-earth orbit, which can offer an uninterrupted coverage and greatly improve monitoring capabilities. 1,2 The traditional on-board beam management technique 3 has been deployed in a DRS to schedule data packets from the DRS to a single satellite. In recent years, the distributed satellite constellation 4 contributes innovative applications (such as disaster prevention and mitigation, military reconnaissance, and removal of space debris) by replacing a single large satellite with several very capable spacecrafts, which opens the door to new applications. 5It can be predicted that a tremendous increase in the number of distributed satellite constellations will impose addition and very diverse requirements on the SBIN. More specially, far more stringent latency and greater capacity requirements are expected to support the real-time data interaction between the distributed satellite constellations, such as the proximity operations and cooperative communications. The different distributed satellite constellations with the unscheduled burst data traffic increase the complexity for beam management, which presents a new challenge for the DRS systems. To tackle the challenges aforementioned above, the multibeam DRS system 6 is ideally suited to deploy such a network as described. The ...

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Recently, the cognitive satellite communication has attracted more attention because of the high spectrum efficiency in the satellite bands. This paper investigates a distributed joint resource allocation algorithm based on the convex optimization theory in the cognitive satellite terrestrial networks under non-ideal spectrum sensing. In the cognitive satellite terrestrial network scenario, satellite users act as secondary users (SUs) who have the ability to transmit over multiple and simultaneous radio access technologies (RATs). Considering primary user activity modeling, the goal of the proposed algorithm is to minimize the end-to-end delay of the SUs in an actual propagation channel scenario. The bandwidth of different RATs and the power of different SUs are jointly allocated, and then, the data being transported by each SU are sent based on the allocated bandwidth and power. The numerical results validate the performance enhancement of the proposed algorithm and show the impact of channel condition parameters on the SUs' delays under non-ideal spectrum sensing. The SUs transmission delays reduce by 81.31% after the resource allocation when the mean of primary user activity matrix is 0.9. INDEX TERMSCognitive satellite communication, resources allocation, primary user activity matrix, convex optimization.

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