5MART: A 5G SMART scheduling framework for optimizing QoS through reinforcement learning
Article
Comsa, I., Trestian, R., Muntean, G. and Ghinea, G. 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. https://doi.org/10.1109/TNSM.2019.2960849
Type | Article |
---|---|
Title | 5MART: A 5G SMART scheduling framework for optimizing QoS through reinforcement learning |
Authors | Comsa, I., Trestian, R., Muntean, G. and Ghinea, G. |
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. |
Keywords | Quality of service; Frequency-domain analysis; 5G mobile communication; Delays; Time-domain analysis; Dynamic scheduling; 5G; radio resource management; machine learning; scheduling; traffic prioritization; QoS optimization |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Journal | IEEE Transactions on Network and Service Management |
ISSN | |
Electronic | 1932-4537 |
Publication dates | |
Online | 19 Dec 2019 |
10 Jun 2020 | |
Publication process dates | |
Deposited | 02 Mar 2020 |
Accepted | 11 Dec 2019 |
Submitted | 28 May 2019 |
Output status | Published |
Accepted author manuscript | File Access Level Open |
Copyright Statement | © 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. |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TNSM.2019.2960849 |
Web of Science identifier | WOS:000542964800034 |
Language | English |
https://repository.mdx.ac.uk/item/88x1w
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