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
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