Elgamal, AS, Alsulami, OZ orcid.org/0000-0002-2096-307X, Qidan, AA et al. (2 more authors) (2021) Q-learning algorithm for resource allocation in WDMA-based optical wireless communication networks. In: 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech). 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech), 08-11 Sep 2021, Bol and Split, Croatia. IEEE ISBN 978-1-6654-4202-2
Abstract
Visible Light Communication (VLC) has been widely investigated during the last decade due to its ability to provide high data rates with low power consumption. In general, resource management is an important issue in cellular networks that can highly effect their performance. In this paper, an optimisation problem is formulated to assign each user to an optimal access point and a wavelength at a given time. This problem can be solved using mixed integer linear programming (MILP). However, using MILP is not considered a practical solution due to its complexity and memory requirements. In addition, accurate information must be provided to perform the resource allocation. Therefore, the optimisation problem is reformulated using reinforcement learning (RL), which has recently received tremendous interest due to its ability to interact with any environment without prior knowledge. In this paper, the resource allocation optimisation problem in VLC systems is investigated using the basic Q-learning algorithm. Two scenarios are simulated to compare the results with the previously proposed MILP model. The results demonstrate the ability of the Q-learning algorithm to provide optimal solutions close to the MILP model without prior knowledge of the system.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 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. |
Keywords: | Visible light communication , resource allocation , MILP , reinforcement learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/H040536/1 EPSRC (Engineering and Physical Sciences Research Council) EP/K016873/1 EPSRC (Engineering and Physical Sciences Research Council) EP/S016570/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Nov 2021 13:56 |
Last Modified: | 24 Nov 2021 13:56 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.23919/splitech52315.2021.9566383 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180746 |