Radio Resource Allocation for 5G Networks Using Deep Reinforcement Learning

被引：5
|
作者：
Munaye, Yirga Yayeh [1 ]
Lin, Hsin-Piao [2 ]
Lin, Ding-Bing [3 ]
Juang, Rong-Terng [4 ]
Tarekegn, Getaneh Berie [5 ]
Jeng, Shiann-Shiun [6 ]
机构：
[1] Bahir Dar Univ, Bahir Dar Inst Technol, Fac Comp, Bahir Dar, Ethiopia
[2] Natl Taipei Univ Technol, Dept Elect Engn, Taipei, Taiwan
[3] Natl Taiwan Univ Sci & Technol, Dept Elect & Comp Engn, Taipei, Taiwan
[4] Feng Chia Univ, Dept Elect Engn, Taichung, Taiwan
[5] Natl Taipei Univ Technol, Dept Elect Engn & Comp Sci, Taipei, Taiwan
[6] Natl Dong Hwa Univ, Dept Elect Engn, Hualien, Taiwan
来源：
2021 30TH WIRELESS AND OPTICAL COMMUNICATIONS CONFERENCE (WOCC 2021) | 2021年
关键词：
resource allocation; deep reinforcement learning; resource slicing; MANAGEMENT; ACCESS;
D O I：
10.1109/WOCC53213.2021.9603111
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：
The emerging 5G mobile network technology is intended to deliver an effective platform for the communication of devices within users. In this study, the proposed method enables the improvement of the connectivity for the IoT with network slicing concepts. The allocation of resources is based on individual network slices specified as audio, texting, video, and browsing. Then, to maximize the average resource allocation performance, a deep reinforcement learning (DRL) optimization method is proposed. For user resource request queue round-robin scheduling algorithm is applied to control the traffic for sharing resources. Finally, this work addresses two leading issues as allocating network slices to the users, balancing resource blocks, and quality of service for fair resource allocation.
引用
收藏
页码：66 / 69
页数：4
相关论文
共 50 条
  • [11] Learning From Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing
    Zhou, Hao
    Erol-Kantarci, Melike
    Poor, H. Vincent
    IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 2022, 8 (04) : 1925 - 1941
  • [12] Resource allocation in 5G cloud-RAN using deep reinforcement learning algorithms: A review
    Khani, Mohsen
    Jamali, Shahram
    Sohrabi, Mohammad Karim
    Sadr, Mohammad Mohsen
    Ghaffari, Ali
    TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2024, 35 (01)
  • [13] Deep Reinforcement Learning-Based Resource Allocation for mm-Wave Dense 5G Networks
    Martyna, Jerzy
    HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2022, 2022, 13469 : 298 - 307
  • [14] Deep Learning Based Cooperative Resource Allocation in 5G Wireless Networks
    Dan Huang
    Yuan Gao
    Yi Li
    Mengshu Hou
    Wanbin Tang
    Shaochi Cheng
    Xiangyang Li
    Yunchuan Sun
    Mobile Networks and Applications, 2022, 27 : 1131 - 1138
  • [15] Deep Learning Based Cooperative Resource Allocation in 5G Wireless Networks
    Huang, Dan
    Gao, Yuan
    Li, Yi
    Hou, Mengshu
    Tang, Wanbin
    Cheng, Shaochi
    Li, Xiangyang
    Sun, Yunchuan
    MOBILE NETWORKS & APPLICATIONS, 2022, 27 (03): : 1131 - 1138
  • [16] 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
  • [17] Reinforcement Learning Approach for Resource Allocation in 5G HetNets
    Allagiotis, Fivos
    Bouras, Christos
    Kokkinos, Vasileios
    Gkamas, Apostolos
    Pouyioutas, Philippos
    2023 INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING, ICOIN, 2023, : 387 - 392
  • [18] Deep Learning based User Slice Allocation in 5G Radio Access Networks
    Matoussi, Salma
    Fajjari, Ilhem
    Aitsaadi, Nadjib
    Langar, Rami
    PROCEEDINGS OF THE 2020 IEEE 45TH CONFERENCE ON LOCAL COMPUTER NETWORKS (LCN 2020), 2020, : 286 - 296
  • [19] A Survey on Applications of Deep Reinforcement Learning in Resource Management for 5G Heterogeneous Networks
    Lee, Ying Loong
    Qin, Donghong
    2019 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC), 2019, : 1856 - 1862
  • [20] Dynamic Resource Aware VNF Placement with Deep Reinforcement Learning for 5G Networks
    Dalgkitsis, Anestis
    Mekikis, Prodromos-Vasileios
    Antonopoulos, Angelos
    Kormentzas, Georgios
    Verikoukis, Christos
    2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2020,
12345