5MART: A 5G SMART scheduling framework for optimizing QoS through reinforcement learning

Comsa, Ioan-Sorin ORCID: https://orcid.org/0000-0002-9121-0286, Trestian, Ramona ORCID: https://orcid.org/0000-0003-3315-3081, Muntean, Gabriel-Miro and Ghinea, Gheorghita ORCID: https://orcid.org/0000-0003-2578-5580 (2020) 5MART: A 5G SMART scheduling framework for optimizing QoS through reinforcement learning. IEEE Transactions on Network and Service Management, 17 (2) . pp. 1110-1124. ISSN 1932-4537 [Article] (doi:10.1109/TNSM.2019.2960849)

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Abstract

The massive growth in mobile data traffic and the heterogeneity and stringency of Quality of Service (QoS) requirements of various applications have put significant pressure on the underlying network infrastructure and represent an important challenge even for the very anticipated 5G networks. In this context, the solution is to employ smart Radio Resource Management (RRM) in general and innovative packet scheduling in particular in order to offer high flexibility and cope with both current and upcoming QoS challenges. Given the increasing demand for bandwidth-hungry applications, conventional scheduling strategies face significant problems in meeting the heterogeneous QoS requirements of various application classes under dynamic network conditions. This paper proposes 5MART, a 5G smart scheduling framework that manages the QoS provisioning for heterogeneous traffic. Reinforcement learning and neural networks are jointly used to find the most suitable scheduling decisions based on current networking conditions. Simulation results show that the proposed 5MART framework can achieve up to 50% improvement in terms of time fraction (in sub-frames) when the heterogeneous QoS constraints are met with respect to other state-of-the-art scheduling solutions.

Item Type: Article
Keywords (uncontrolled): 5G, radio resource management, machine learning, scheduling, traffic prioritization, QoS optimization
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 29440
Notes on copyright: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Ramona Trestian
Date Deposited: 02 Mar 2020 13:07
Last Modified: 18 Sep 2020 19:06
URI: https://eprints.mdx.ac.uk/id/eprint/29440

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