Elgamal, AS, Alsulami, OZ orcid.org/0000-0002-2096-307X, Qidan, AA et al. (2 more authors) (2021) Reinforcement Learning for Resource Allocation in Steerable Laser-Based Optical Wireless Systems. In: 2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). 2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 12-17 Sep 2021, Ontario, Canada. IEEE ISBN 978-1-6654-4865-9
Abstract
Vertical Cavity Surface Emitting Lasers (VCSELs) have demonstrated suitability for data transmission in indoor optical wireless communication (OWC) systems due to the high modulation bandwidth and low manufacturing cost of these sources. Specifically, resource allocation is one of the major challenges that can affect the performance of multi-user optical wireless systems. In this paper, an optimisation problem is formulated to optimally assign each user to an optical access point (AP) composed of multiple VCSELs within a VCSEL array at a certain time to maximise the signal to interference plus noise ratio (SINR). In this context, a mixed-integer linear programming (MILP) model is introduced to solve this optimisation problem. Despite the optimality of the MILP model, it is considered impractical due to its high complexity, high memory and full system information requirements. Therefore, reinforcement Learning (RL) is considered, which recently has been widely investigated as a practical solution for various optimisation problems in cellular networks due to its ability to interact with environments with no previous experience. In particular, a Q-learning (QL) algorithm is investigated to perform resource management in a steerable VCSEL-based OWC systems. The results demonstrate the ability of the QL algorithm to achieve optimal solutions close to the MILP model. Moreover, the adoption of beam steering, using holograms implemented by exploiting liquid crystal devices, results in further enhancement in the performance of the network considered.
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: | VCSEL , OWC , 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 14:08 |
Last Modified: | 08 Dec 2021 01:32 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ccece53047.2021.9569123 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180744 |