Joint Access Point Selection and Resource Allocation in MEC-Assisted Network:A Reinforcement Learning Based Approach

作     者：Zexu Li Chunjing Hu Wenbo Wang Yong Li Guiming Wei Zexu Li;Chunjing Hu;Wenbo Wang;Yong Li;Guiming Wei

作者机构：Key Laboratory of Universal Wireless CommunicationsBeijing University of Posts and TelecommunicationsBeijing 100876China China Academy of Information and Communications TechnologyBeijing 100191China 

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：205-218页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 081104[工学-模式识别与智能系统] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0811[工学-控制科学与工程] 

基　　金：supported in part by the National Natural Science Foundation of China under Grant 61671074 in part by Project No.A01B02C01202015D0。 

主　　题：mobile edge computing joint resource allocation reinforcement learning 

摘      要：A distributed reinforcement learning(RL)based resource management framework is proposed for a mobile edge computing(MEC)system with both latency-sensitive and latency-insensitive services.We investigate joint optimization of both computing and radio resources to achieve efficient on-demand matches of multi-dimensional resources and diverse requirements of users.A multi-objective integer programming problem is formulated by two subproblems,i.e.,access point(AP)selection and subcarrier allocation,which can be solved jointly by our proposed distributed RL-based approach with a heuristic iteration algorithm.The proposed algorithm allows for the reduction in complexity since each user needs to consider only its own selection of AP without knowing full global information.Simulation results show that our algorithm can achieve near-optimal performance while reducing computational complexity significantly.Compared with other algorithms that only optimize either of the two sub-problems,the proposed algorithm can serve more users with much less power consumption and content delivery latency.