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

作者：Hu, Xin; Liu, Shuaijun^*; Chen, Rong; Wang, Weidong; Wang, Chunting

来源：IEEE Communications Letters, 2018, 22(8): 1612-1615.

DOI：10.1109/LCOMM.2018.2844243

• Dynamic resource allocation (DRA)
• deep reinforcement learning (DRL)
• state reformulation
• multibeam satellite (MBS)

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 NIBS 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.

• 出版日期2018-8
• 单位北京邮电大学