An online power allocation algorithm based on deep reinforcement learning in multibeam satellite systems,International Journal of Satellite Communications and Networking

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An online power allocation algorithm based on deep reinforcement learning in multibeam satellite systems
International Journal of Satellite Communications and Networking ( IF 0.9 ) Pub Date : 2020-06-08 , DOI: 10.1002/sat.1352
Pei Zhang ₁ , Xiaohui Wang ₁ , Zhiguo Ma ₂ , Shuaijun Liu ₃ , Junde Song ₁

Affiliation

Dynamic power allocation (DPA) is the key technique to improve the system throughput by matching the offered capacity with that required among distributed beams in multibeam satellite systems. Existing power allocation studies tend to adopt the metaheuristic optimization algorithms such as the genetic algorithm. The achieved DPA cannot adapt to the dynamic environments due to the varying traffic demands and the channel conditions. To solve this problem, an online algorithm named deep reinforcement learning‐based dynamic power allocation (DRL‐DPA) algorithm is proposed in this paper. The key idea of the proposed DRL‐DPA lies in the online power allocation decision making other than the offline way of the traditional metaheuristic methods. Simulation results show that the proposed DRL‐DPA algorithm can improve the system performance in terms of system throughput and power consumption in multibeam satellite systems.

中文翻译：

基于深度增强学习的多波束卫星系统在线功率分配算法

动态功率分配（DPA）是通过将提供的容量与多波束卫星系统中分布式波束之间所需的容量进行匹配来提高系统吞吐量的关键技术。现有的功率分配研究倾向于采用元启发式优化算法，例如遗传算法。由于流量需求和信道条件的变化，获得的DPA无法适应动态环境。为解决这一问题，本文提出了一种基于深度强化学习的动态功率分配（DRL-DPA）算法。提出的DRL-DPA的关键思想在于在线功率分配决策，而不是传统的元启发式方法的离线方法。

更新日期：2020-06-08

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