M. Alsenwi, N. H. Tran, M. Bennis, S. R. Pandey, A. K. Bairagi and C. S. Hong, "Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach," in IEEE Transactions on Wireless Communications, vol. 20, no. 7, pp. 4585-4600, July 2021, doi: 10.1109/TWC.2021.3060514
Intelligent resource slicing for eMBB and URLLC coexistence in 5G and beyond : a deep reinforcement learning based approach
|Author:||Alsenwi, Madyan1; Tran, Nguyen H.2; Bennis, Mehdi3,1;|
1Department of Computer Science and Engineering, Kyung Hee University, Yongin 17104, South Korea
2School of Computer Science, University of Sydney, NSW 2006, Australia
3Department of Communications Engineering, University of Oulu, FI-90014 Oulu, Finland
4Discipline of Computer Science and Engineering, Khulna University, Khulna 9208, Bangladesh
|Online Access:||PDF Full Text (PDF, 1.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021101150546
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-10-11
In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB services focus on high data rates, URLLC is very strict in terms of latency and reliability. In view of this, the resource slicing problem is formulated as an optimization problem that aims at maximizing the eMBB data rate subject to a URLLC reliability constraint, while considering the variance of the eMBB data rate to reduce the impact of immediately scheduled URLLC traffic on the eMBB reliability. To solve the formulated problem, an optimization-aided Deep Reinforcement Learning (DRL) based framework is proposed, including: 1) eMBB resource allocation phase, and 2) URLLC scheduling phase. In the first phase, the optimization problem is decomposed into three subproblems and then each subproblem is transformed into a convex form to obtain an approximate resource allocation solution. In the second phase, a DRL-based algorithm is proposed to intelligently distribute the incoming URLLC traffic among eMBB users. Simulation results show that our proposed approach can satisfy the stringent URLLC reliability while keeping the eMBB reliability higher than 90%.
IEEE transactions on wireless communications
|Pages:||4585 - 4600|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was partially supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2019-0-01287, Evolvable Deep Learning Model Generation Platform for Edge Computing) and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. No. 2020R1A4A1018607).
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