Deep Reinforcement Learning-Based Resource Allocation for mm-Wave Dense 5G Networks

被引:0
|
作者
Martyna, Jerzy [1 ]
机构
[1] Jagiellonian Univ, Inst Comp Sci, Fac Math & Comp Sci, ul Prof S Lojasiewicza 6, PL-30348 Krakow, Poland
关键词
D O I
10.1007/978-3-031-15471-3_26
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In microwave technology, directional beams are used for the propagation of radio waves. Nevertheless, significant errors occur in localizing the receiver. The paper presents the method for radio resource allocation and beam management based on the double deep Q-learning algorithm. Simulation studies confirm that the proposed method significantly improves the efficiency of the millimeter 5G network.
引用
收藏
页码:298 / 307
页数:10
相关论文
共 50 条
  • [21] Reinforcement Learning-Based Optimization for Drone Mobility in 5G and Beyond Ultra-Dense Networks
    Tanveer, Jawad
    Haider, Amir
    Ali, Rashid
    Kim, Ajung
    CMC-COMPUTERS MATERIALS & CONTINUA, 2021, 68 (03): : 3807 - 3823
  • [22] Caching and Computing Resource Allocation in Cooperative Heterogeneous 5G Edge Networks Using Deep Reinforcement Learning
    Bose, Tushar
    Chatur, Nilesh
    Baberwal, Sonil
    Adhya, Aneek
    IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 2024, 21 (04): : 4161 - 4178
  • [23] Resource allocation for UAV-assisted 5G mMTC slicing networks using deep reinforcement learning
    Rohit Kumar Gupta
    Saubhik Kumar
    Rajiv Misra
    Telecommunication Systems, 2023, 82 : 141 - 159
  • [24] Deep Reinforcement Learning Based Resource Allocation with Radio Remote Head Grouping and Vehicle Clustering in 5G Vehicular Networks
    Park, Hyebin
    Lim, Yujin
    ELECTRONICS, 2021, 10 (23)
  • [25] Intimacy-based Resource Allocation for Network Slicing in 5G via Deep Reinforcement Learning
    He, Nan
    Yang, Song
    Li, Fan
    Chen, Xu
    IEEE NETWORK, 2021, 35 (06): : 111 - 118
  • [26] Deep Reinforcement Learning-Based Network Slicing for beyond 5G
    Suh, Kyungjoo
    Kim, Sunwoo
    Ahn, Yongjun
    Kim, Seungnyun
    Ju, Hyungyu
    Shim, Byonghyo
    IEEE Access, 2022, 10 : 7384 - 7395
  • [27] Planar Wideband mm-Wave Antennas for mm-Wave 5G applications
    Hao, Zhang-Cheng
    2019 INTERNATIONAL CONFERENCE ON MICROWAVE AND MILLIMETER WAVE TECHNOLOGY (ICMMT 2019), 2019,
  • [28] Deep Reinforcement Learning-Based Network Slicing for Beyond 5G
    Suh, Kyungjoo
    Kim, Sunwoo
    Ahn, Yongjun
    Kim, Seungnyun
    Ju, Hyungyu
    Shim, Byonghyo
    IEEE ACCESS, 2022, 10 : 7384 - 7395
  • [29] DISTRIBUTED RESOURCE ALLOCATION IN 5G NETWORKS WITH MULTI-AGENT REINFORCEMENT LEARNING
    Menard, Jon
    Al-Habashna, Ala'a
    Wainer, Gabriel
    Boudreau, Gary
    PROCEEDINGS OF THE 2022 ANNUAL MODELING AND SIMULATION CONFERENCE (ANNSIM'22), 2022, : 802 - 813
  • [30] Resource allocation in mmWave 5G IAB networks: A reinforcement learning approach based on column generation
    Zhang, Bibo
    Devoti, Francesco
    Filippini, Ilario
    De Donno, Danilo
    COMPUTER NETWORKS, 2021, 196