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An online power allocation algorithm based on deep reinforcement learning in multibeam satellite systems
Author(s) -
Zhang Pei,
Wang Xiaohui,
Ma Zhiguo,
Liu Shuaijun,
Song Junde
Publication year - 2020
Publication title -
international journal of satellite communications and networking
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.388
H-Index - 39
eISSN - 1542-0981
pISSN - 1542-0973
DOI - 10.1002/sat.1352
Subject(s) - computer science , reinforcement learning , throughput , key (lock) , genetic algorithm , channel (broadcasting) , metaheuristic , algorithm , matching (statistics) , power (physics) , distributed computing , artificial intelligence , machine learning , wireless , computer network , telecommunications , statistics , computer security , mathematics , physics , quantum mechanics
Summary 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.