Vol. 17, No. 9, September 30, 2023
10.3837/tiis.2023.09.014,
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Abstract
As 5G mobile systems carry multiple services and applications, numerous user, and
application types with varying quality of service requirements inside a single physical network
infrastructure are the primary problem in constructing 5G networks. Radio Access Network
(RAN) slicing is introduced as a way to solve these challenges. This research focuses on
optimizing RAN slices within a singular physical cell for vehicle-to-everything (V2X) and
enhanced mobile broadband (eMBB) UEs, highlighting the importance of adept resource
management and allocation for the evolving landscape of 5G services. We put forth two unique
strategies: one being offline network slicing, also referred to as standard network slicing, and
the other being Online reinforcement learning (RL) network slicing. Both strategies aim to
maximize network efficiency by gathering network model characteristics and augmenting
radio resources for eMBB and V2X UEs. When compared to traditional network slicing, RL
network slicing shows greater performance in the allocation and utilization of UE resources.
These steps are taken to adapt to fluctuating traffic loads using RL strategies, with the ultimate
objective of bolstering the efficiency of generic 5G services.
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Cite this article
[IEEE Style]
W. Zhou, A. Islam, K. Chang, "Real-time RL-based 5G Network Slicing Design and Traffic Model Distribution: Implementation for V2X and eMBB Services," KSII Transactions on Internet and Information Systems, vol. 17, no. 9, pp. 2573-2589, 2023. DOI: 10.3837/tiis.2023.09.014.
[ACM Style]
WeiJian Zhou, Azharul Islam, and KyungHi Chang. 2023. Real-time RL-based 5G Network Slicing Design and Traffic Model Distribution: Implementation for V2X and eMBB Services. KSII Transactions on Internet and Information Systems, 17, 9, (2023), 2573-2589. DOI: 10.3837/tiis.2023.09.014.
[BibTeX Style]
@article{tiis:56003, title="Real-time RL-based 5G Network Slicing Design and Traffic Model Distribution: Implementation for V2X and eMBB Services", author="WeiJian Zhou and Azharul Islam and KyungHi Chang and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2023.09.014}, volume={17}, number={9}, year="2023", month={September}, pages={2573-2589}}