Reinforcement Learning for Resource Allocation in Steerable Laser-Based Optical Wireless Systems

Abdelrahman S. Elgamal, Osama Z. Alsulami, Ahmad Adnan Qidan, Taisir E.H. El-Gorashi, Jaafar M.H. Elmirghani

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2021 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2021
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-6
Number of pages6
ISBN (Electronic)9781665448642
DOIs
Publication statusPublished - 12 Sept 2021
Externally publishedYes
Event2021 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2021 - Virtual, Online, Canada
Duration: 12 Sept 202117 Sept 2021

Publication series

NameCanadian Conference on Electrical and Computer Engineering
Volume2021-September
ISSN (Print)0840-7789

Conference

Conference2021 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2021
Country/TerritoryCanada
CityVirtual, Online
Period12/09/2117/09/21

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