TY - GEN
T1 - Reinforcement Learning for Resource Allocation in Steerable Laser-Based Optical Wireless Systems
AU - Elgamal, Abdelrahman S.
AU - Alsulami, Osama Z.
AU - Qidan, Ahmad Adnan
AU - El-Gorashi, Taisir E.H.
AU - Elmirghani, Jaafar M.H.
N1 - Funding Information:
ACKNOWLEDGMENTS This work has been supported in part by the Engineering and Physical Sciences Research Council (EPSRC), in part by the INTERNET project under Grant EP/H040536/1, and in part by the STAR project under Grant EP/K016873/1 and in part by the TOWS project under Grant EP/S016570/1. All data are provided in full in the results section of this paper. ASE author would like to acknowledge EPSRC for funding his PhD scholarship. OZA would like to thank Umm Al-Qura University in the Kingdom of Saudi Arabia for funding his PhD scholarship.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/9/12
Y1 - 2021/9/12
N2 - 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.
AB - 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.
KW - MILP
KW - OWC
KW - reinforcement learning
KW - resource allocation
KW - VCSEL
UR - http://www.scopus.com/inward/record.url?scp=85118453267&partnerID=8YFLogxK
U2 - 10.1109/CCECE53047.2021.9569123
DO - 10.1109/CCECE53047.2021.9569123
M3 - Conference contribution
AN - SCOPUS:85118453267
T3 - Canadian Conference on Electrical and Computer Engineering
SP - 1
EP - 6
BT - 2021 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2021
PB - IEEE, Institute of Electrical and Electronics Engineers
T2 - 2021 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2021
Y2 - 12 September 2021 through 17 September 2021
ER -