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

TL;DR

This paper explores using reinforcement learning, specifically Q-learning, to optimize resource allocation in steerable VCSEL-based optical wireless systems, achieving near-optimal performance and enhanced network efficiency.

Contribution

It introduces a Q-learning approach for resource management in VCSEL-based optical wireless systems, providing a practical alternative to complex optimization models.

Findings

Q-learning achieves near-optimal resource allocation.

Beam steering with holograms improves network performance.

Reinforcement learning offers a feasible solution for complex optimization.

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 optimization 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.