Joint User Scheduling and Beam Selection in mmWave Networks Based on Multi-Agent Reinforcement Learning

被引：6
|
作者：
Xu, Chunmei [1 ,2 ]
Liu, Shengheng [1 ,2 ]
Zhang, Cheng [1 ,2 ]
Huang, Yongming [1 ,2 ]
Yang, Luxi [1 ,2 ]
机构：
[1] Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Peoples R China
[2] Purple Mt Labs, Nanjing 210096, Peoples R China
来源：
2020 IEEE 11TH SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP (SAM) | 2020年
基金：
中国国家自然科学基金; 国家重点研发计划;
关键词：
Millimeter wave communication; user scheduling; beam selection; distributed algorithm; multi-agent reinforcement learning; OPTIMIZATION;
D O I：
10.1109/sam48682.2020.9104386
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we consider a multi-cell downlink mmWave communication network and investigate an efficient transmission scheme for all base stations. Since the beams are highly directed with respected to the user equipments, user scheduling and beam selection strategy should be jointly considered. The objective is to develop the joint user scheduling and beam selection strategy that minimizes the long-term average delay cost while satisfying the instantaneous quality of service constraint of each user. To achieve the long-term performance, a distributed algorithm is proposed to develop the joint strategy based on multi-agent reinforcement learning. Simulation results validate the effectiveness of the proposed intelligent distributed method.
引用
收藏
页数：5
相关论文
共 50 条
  • [41] Multi-AGV Scheduling based on Hierarchical Intrinsically Rewarded Multi-Agent Reinforcement Learning
    Zhang, Jiangshan
    Guo, Bin
    Sun, Zhuo
    Li, Mengyuan
    Liu, Jiaqi
    Yu, Zhiwen
    Fan, Xiaopeng
    2022 IEEE 19TH INTERNATIONAL CONFERENCE ON MOBILE AD HOC AND SMART SYSTEMS (MASS 2022), 2022, : 155 - 161
  • [42] Symmetry-Augmented Multi-Agent Reinforcement Learning for Scalable UAV Trajectory Design and User Scheduling
    Zhou, Xuanhan
    Xiong, Jun
    Zhao, Haitao
    Yan, Chao
    Wei, Jibo
    IEEE TRANSACTIONS ON MOBILE COMPUTING, 2024, 23 (12) : 14127 - 14144
  • [43] Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning
    Yuan, Mingqi
    Cao, Qi
    Pun, Man-On
    Chen, Yi
    APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING, 2022, 11 (01)
  • [44] Multi-Agent Reinforcement Learning
    Stankovic, Milos
    2016 13TH SYMPOSIUM ON NEURAL NETWORKS AND APPLICATIONS (NEUREL), 2016, : 43 - 43
  • [45] Joint Frequency Assignment and Power Allocation Based on Multi-Agent Deep Reinforcement Learning for Multi-Beam Satellite Systems
    Li, Yuanjun
    Yang, Dewei
    Yang, Haowen
    Kuang, Jingming
    2023 IEEE 97TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-SPRING, 2023,
  • [46] Multi-agent Reinforcement Learning Model for Effective Action Selection
    Youk, Sang Jo
    Lee, Bong Keun
    INFORMATION SECURITY AND ASSURANCE, 2010, 76 : 309 - +
  • [47] Decentralised grid scheduling approach based on multi-agent reinforcement learning and gossip mechanism
    Wu, Jun
    Xu, Xin
    CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY, 2018, 3 (01) : 8 - 17
  • [48] Multi-agent reinforcement learning based textile dyeing workshop dynamic scheduling method
    He J.
    Zhang J.
    Zhang P.
    Zheng P.
    Wang M.
    Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2023, 29 (01): : 61 - 74
  • [49] Multi-Agent Deep Reinforcement Learning for Joint Decoupled User Association and Trajectory Design in Full-Duplex Multi-UAV Networks
    Dai, Chen
    Zhu, Kun
    Hossain, Ekram
    IEEE TRANSACTIONS ON MOBILE COMPUTING, 2023, 22 (10) : 6056 - 6070
  • [50] Multi-Agent Reinforcement Learning for Network Selection and Resource Allocation in Heterogeneous Multi-RAT Networks
    Allahham, Mhd Saria
    Abdellatif, Alaa Awad
    Mhaisen, Naram
    Mohamed, Amr
    Erbad, Aiman
    Guizani, Mohsen
    IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 2022, 8 (02) : 1287 - 1300
12345