A Cross Q-Learning Assisted Resource Allocation for User-Centric Optical Wireless Communication Networks

Abstract

The user-centric (UC) association in optical wireless communication (OWC) forms amorphous cells (A-Cells) by considering the dynamic distribution and load demand of user equipments (UEs). This philosophy offers advantages over the conventional network-centric (NC) association that purely relies on a pre-defined and fixed network configuration, in terms of alleviating undesired inter-cell interference (ICI) and achieving superior system performance. However, constructing the optimal A-Cells for a given OWC network, including determining the appropriate number of A-Cells associated to their contained UEs, is deeply integrated with the UEs’ distribution and transmission conditions. To address the intractable issue, in this paper, we conceive an adaptive UC-OWC network that relies on a feedback-guided iterative framework, which is capable of jointly optimizing A-Cells formation, modulation-mode assignment and power allocation strategies. For the sake of attaining the optimized throughput of this adaptive network, we initialize the UC association by the designed k-means based genetic algorithm (KGA), which can then be iteratively adjusted based on the throughput feedback obtained via our proposed multi-user cross Q-learning (MUCQ) resource allocation algorithm. Simulation results indicate that, compared to conventional counterparts, our adaptive UC-OWC network is able to significantly improve throughput performance and reduce outage probability.