Dynamic Resource Allocation for Multibeam Satellite Communication Systems

• Published in: IEEE internet of things journal 2024-11, Vol.11 (22), p.36907-36921
• Main Authors: Zhang, Siya, Chai, Rong, Liang, Chengchao, Chen, Qianbin
• Format: Article
• Language: English
• Subjects: Algorithms; Beam illumination; Communications systems; Cost function; Design optimization; Heuristic algorithms; Illumination; Internet of Things; Lighting; Markov processes; multibeam satellite; Optimization; parameterized deep Q-network; power allocation; Provisioning; proximal policy optimization; Resource allocation; Resource management; Resource utilization; Satellite communications; Satellites; subchannel allocation; Throughput; User groups
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3433022

Multibeam satellite communication systems have been received widespread attention due to their high throughput and efficient resource utilization. In this article, we investigate the beam illumination and resource allocation problem in multibeam satellite communication systems. By jointly considering user position and service characteristics, an optics-based initial user grouping algorithm is proposed. To enhance beam coverage performance, a minimum circle algorithm is proposed to optimally design satellite beam positions and coverage radius. Given the obtained user grouping strategy, we address the difference between random user service demands and service provisioning capability of the system, and define system cost function. The joint beam illumination, subchannel, and power allocation problem is formulated as a system cost function minimization problem. To solve the formulated optimization problem, we introduce aggregate nodes to describe the characteristics of user groups, and address the beam illumination and power allocation problem of user groups. The problem is modeled as a mixed-space Markov decision process (MDP), and a parameterized deep Q-network-based joint beam illumination and power allocation algorithm is proposed. Based on the obtained resource allocation strategy for user groups, we then design user-oriented subchannel and power allocation strategy. To this end, we model the optimization problem as an MDP and propose a double deep Q-network (DDQN) algorithm-based algorithm. To address the concern that the DDQN algorithm may reach a local optimum, proximal policy optimization algorithms with discrete action space and continuous action space are proposed. Simulation results validate the effectiveness of the proposed algorithms.