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
来源
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2022 | 2022年 / 13469卷
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
[31]   Reinforcement Learning Approach for Resource Allocation in 5G HetNets [J].
Allagiotis, Fivos ;
Bouras, Christos ;
Kokkinos, Vasileios ;
Gkamas, Apostolos ;
Pouyioutas, Philippos .
2023 INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING, ICOIN, 2023, :387-392
[32]   Analysis of TCP Performance in 5G mm-wave Mobile Networks [J].
Jimenez Mateo, Pablo ;
Fiandrino, Claudio ;
Widmer, Joerg .
ICC 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2019,
[33]   Reinforcement Learning-based Computation Resource Allocation Scheme for 5G Fog-Radio Access Network [J].
Khumalo, Nosipho ;
Oyerinde, Olutayo ;
Mfupe, Luzango .
2020 FIFTH INTERNATIONAL CONFERENCE ON FOG AND MOBILE EDGE COMPUTING (FMEC), 2020, :353-355
[34]   Reinforcement Learning-Based Radio Resource Control in 5G Vehicular Network [J].
Zhou, Yibo ;
Tang, Fengxiao ;
Kawamoto, Yuichi ;
Kato, Nei .
IEEE WIRELESS COMMUNICATIONS LETTERS, 2020, 9 (05) :611-614
[35]   Towards Trustworthy Reinforcement Learning-based Resource Management in Beyond 5G [J].
Perez-Romero, Jordi ;
Salient, Oriol ;
Vila, Irene ;
Kartsakli, Elli ;
Tuna, Omer Faruk ;
Mohalik, Swamp Kumar ;
Tao, Xin .
2024 JOINT EUROPEAN CONFERENCE ON NETWORKS AND COMMUNICATIONS & 6G SUMMIT, EUCNC/6G SUMMIT 2024, 2024, :806-811
[36]   Resource allocation strategy based on deep reinforcement learning in 6G dense network [J].
Yang F. ;
Yang C. ;
Huang J. ;
Zhang S. ;
Yu T. ;
Zuo X. ;
Yang C. .
Tongxin Xuebao/Journal on Communications, 2023, 44 (08) :215-227
[37]   A Deep-Learning-Based Radio Resource Assignment Technique for 5G Ultra Dense Networks [J].
Zhou, Yibo ;
Fadlullah, Zubair Md. ;
Mao, Bomin ;
Kato, Nei .
IEEE NETWORK, 2018, 32 (06) :28-34
[38]   Deep Reinforcement Learning Based Resource Allocation for 5G V2V Groupcast Communications [J].
Wu, Shang-Huan ;
Hwang, Ren-Hung ;
Wang, Chih-Yu ;
Chou, Ching-Hsuan .
2023 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS, ICNC, 2023, :1-6
[39]   Joint resource allocation for emotional 5G IoT systems using deep reinforcement learning [J].
Yang, Ziyan ;
Mei, Haibo ;
Wang, Wenyong ;
Zhou, Dongdai ;
Yang, Kun .
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2021, 12 (12) :3517-3528
[40]   Joint resource allocation for emotional 5G IoT systems using deep reinforcement learning [J].
Ziyan Yang ;
Haibo Mei ;
Wenyong Wang ;
Dongdai Zhou ;
Kun Yang .
International Journal of Machine Learning and Cybernetics, 2021, 12 :3517-3528