A Deep Reinforcement Learning-Based Framework for Dynamic Resource Allocation in Multibeam Satellite Systems

• Published in: IEEE communications letters Vol. 22; no. 8; pp. 1612 - 1615
• Main Authors: Hu, Xin, Liu, Shuaijun, Chen, Rong, Wang, Weidong, Wang, Chunting
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computer simulation; deep reinforcement learning (DRL); Dynamic resource allocation (DRA); Dynamic scheduling; Feature extraction; Heuristic algorithms; Iterative methods; multibeam satellite (MBS); Optimization; Quality of service; Resource allocation; Resource management; Satellites; state reformulation; Tensile stress
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2018.2844243

Dynamic resource allocation (DRA) is the key technology to improve the network performance in resource-limited multibeam satellite (MBS) systems. The aim is to find a policy that maximizes the expected long-term resource utilization. Existing iterative metaheuristics DRA optimization algorithms are not practical due to the high computational complexity. To solve the problem of unknown dynamics and prohibitive computation, a deep reinforcement learning-based framework (DRLF) is proposed for DRA problems in MBS systems. A novel image-like tensor reformulation on the system environments is adopted to extract traffic spatial and temporal features. A use case of dynamic channel allocation in DRLF is simulated and shows the effectiveness of the proposed DRLF in time-varying scenarios.