Two-Agent DRL for Power Allocation and IRS Orientation in Dynamic NOMA-Based OWC Networks

Intelligent reflecting surfaces (IRSs) technology has been considered a promising solution in visible light communication (VLC) systems due to its potential to overcome the line-of-sight (LoS) blockage issue and enhance coverage. Moreover, integrating IRS with a downlink non-orthogonal multiple access (NOMA) transmission technique for multi-users is a smart solution to achieve a high sum rate and improve system performance. In this paper, a dynamic IRS-assisted indoor NOMA-VLC system is modeled, and an optimization problem is formulated to maximize sum energy efficiency (SEE) and fairness among multiple mobile users under power allocation and IRS mirror orientation constraints. Due to the non-convex nature of the optimization problem and the non-linearity of the constraints, conventional optimization methods are impractical for real-time solutions. Therefore, a two-agent deep reinforcement learning (DRL) algorithm is designed for optimizing power allocation and IRS orientation based on centralized training with decentralized execution to obtain fast and real-time solutions in dynamic environments. The results show the superior performance in terms of SEE, sum rate and fairness index of the proposed DRL algorithm compared to standard DRL algorithms and conventional algorithms typically used for resource allocation in wireless communication. The results also show that the proposed two-agent DRL algorithm achieves higher performance compared to deployments without IRS and with randomly oriented IRS elements.