Sequential dynamic resource allocation in multi-beam satellite systems:A learning-based optimization method  

作　　者：Yixin HUANG Shufan WU Zhankui ZENG Zeyu KANG Zhongcheng MU Hai HUANG 

机构地区：[1]School of Aeronautics and Astronautics,Shanghai Jiao Tong University,Shanghai 200240,China [2]Shanghai Academy of Spaceflight Technology,Shanghai 200240,China [3]School of Astronautics,Beihang University,Beijing 100191,China

出　　处：《Chinese Journal of Aeronautics》2023年第6期288-301,共14页中国航空学报（英文版）

基　　金：co-supported by the National Natural Science Foundation of China(No.U20B2056);the Office of Military and Civilian Integration Development Committee of Shanghai,China(No.2020-jmrh1-kj25).

摘　　要：Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency resources.One of the primary challenges accompanying with Multi-Beam Satellites(MBS)is an efficient Dynamic Resource Allocation(DRA)strategy.This paper presents a learning-based Hybrid-Action Deep Q-Network(HADQN)algorithm to address the sequential decision-making optimization problem in DRA.By using a parameterized hybrid action space,HADQN makes it possible to schedule the beam pattern and allocate transmitter power more flexibly.To pursue multiple long-term QoS requirements,HADQN adopts a multi-objective optimization method to decrease system transmission delay,loss ratio of data packets and power consumption load simultaneously.Experimental results demonstrate that the proposed HADQN algorithm is feasible and greatly reduces in-orbit energy consumption without compromising QoS performance.

关 键 词：Beam hopping Deep reinforcement learning Dynamic resource allocation Mixed-integer programming Multi-beam satellite systems Multi-objective optimization 

分 类 号：V474[航空宇航科学与技术—飞行器设计] V52