Reinforcement Learning-Based Handover Strategy for Space-Ground Integration Network with Large-scale Constellations  

作　　者：Xiao Jia Di Zhou Min Sheng Yan Shi Ningyuan Wang Jiandong Li 

机构地区：[1]State Key Laboratory of Integrated Service Networks,Xidian University,Xi’an 710071,China [2]Institute of Telecommunication and Navigation Satellites,China Academy of Space Technology,Beijing 100000,Chin

出　　处：《Journal of Communications and Information Networks》2022年第4期421-432,共12页通信与信息网络学报（英文）

基　　金：This work was supported in part by the National Key R&D Program of China under Grant 2020YFB1806100;in part by the Natural Science Foundation of China under Grant U19B2025 and Grant 62001347;in part by Key Research and Development Program of Shaanxi(Program No.2022ZDLGY05-02,and Program No.2021KWZ-05).

摘　　要：The space-ground integration network(SGIN)with large-scale constellations has become an important topic of the next generation mobile communication technology,in which the handover technology between the satellite and the ground is the key technology to ensure the continuity of user service.However,compared with the ground base station’s coverage of users,satellites have larger coverage and propagation delay,and large-scale constellations make multiple selectable service satellites above the same user.These phenomena bring great challenges to the handover algorithm.This paper designs a reinforcement learning-based multi-attribute satellite-ground handover strategy(RLMSGHS)for SGIN with large-scale constellations.Firstly,users are clustered with the attributes of location,speed,and bandwidth demand.Then,the handover decision can be made based on the proposed RLMSGHS according to the attributes of received signal strength(RSS),speed,network bandwidth utilization and,handover cost.Finally,the simulation results demonstrate that the heavy decision-making burden caused by the large-scale growth of users in the SGIN is significantly reduced.The multi-attribute handover decision in the SGIN is realized,which reduces the handover demand of users and improves the resource utilization rate of the SGIN.

关 键 词：SGIN large-scale constellations satelliteground handover CLUSTER reinforcement learning 

分 类 号：TN92[电子电信—通信与信息系统]