| 한글제목(Korean Title) |
Dynamic Resource Slicing of eMBB/URLLC Traffics in 5G Wireless Networks: A Reinforcement Learning Based Approach |
| 영문제목(English Title) |
Dynamic Resource Slicing of eMBB/URLLC Traffics in 5G Wireless Networks: A Reinforcement Learning Based Approach |
| 저자(Author) |
Madyan Alsenwi
Shashi Raj Pandey
Yan Kyaw Tun
Choong Seon Hong
|
| 원문수록처(Citation) |
VOL 46 NO. 01 PP. 1233 ~ 1235 (2019. 06) |
한글내용
(Korean Abstract) |
Ultra Reliable Low Latency Communications (URLLC) is a 5G wireless networks service that accommodates applications having strict reliability and latency requirements such as industrial IoT, virtual reality, and autonomous vehicles. URLLC aims low latency and high reliability transmissions. In this paper, the dynamic multiplexing of URLLC and enhanced Mobile Broad Band (eMBB) is tackled. We model the problem as a Markov Decision Process. Moreover, We propose a weighted formulation that takes into account both the total average data rate of eMBB traffic and the URLLC reliability. To this end, We propose a Q-learning algorithm where the gNB learns the optimal resource allocation to eMBB and URLLC traffics. The simulation results illustrate the performance of the proposed algorithm. |
영문내용
(English Abstract) |
|
| 키워드(Keyword) |
5G New Radio
eMBB
URLLC
Resource Slicing
Q-learning
|
| 파일첨부 |
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