User-Level Scheduling and Resource Allocation for Multi-Beam Satellite Systems with Full Frequency Reuse

作     者：Tao Leng Yanan Wang Dongwei Hu Gaofeng Cui Weidong Wang Tao Leng;Yanan Wang;Dongwei Hu;Gaofeng Cui;Weidong Wang

作者机构：School of Electronic EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Universal Wireless Communications(Beijing University of Posts and Telecommunications)Beijing 100876China Science and Technology on Information Transmission and Dissemination in Communication Networks LaboratoryHebei 050000China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2022年第19卷第6期

页      面：179-192页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 

基　　金：supported in part by the National Natural Science Foundation of China under Grant 62171052,Grant 61971054 Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory Foundation under Grant HHX21641X002。 

主　　题：multi-beam satellite full frequency reuse inter-beam interference latency optimization deep reinforcement learning 

摘      要：Multi-beam satellite communication systems can improve the resource utilization and system capacity effectively.However,the inter-beam interference,especially for the satellite system with full frequency reuse,will degrade the system performance greatly due to the characteristics of multi-beam satellite antennas.In this article,the user scheduling and resource allocation of a multi-beam satellite system with full frequency reuse are jointly studied,in which all beams can use the full bandwidth.With the strong inter-beam interference,we aim to minimize the system latency experienced by the users during the process of data downloading.To solve this problem,deep reinforcement learning is used to schedule users and allocate bandwidth and power resources to mitigate the inter-beam interference.The simulation results are compared with other reference algorithms to verify the effectiveness of the proposed algorithm.