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Adaptive DRL for IRS Mirror Orientation in Dynamic OWC Networks

Ahrar N. Hamad, Ahmad Adnan Qidan, Taisir E. H. El-Gorashi, Jaafar M. H. Elmirghani

TL;DR

This work tackles dynamic indoor VLC with LoS blockages by employing a mirror-based IRS to steer reflected signals toward mobile users. It formulates the problem as an MDP and develops a DRL algorithm based on deterministic policy gradients to continuously orient IRS mirrors for multiple users under blockage and QoS constraints. The results show the proposed DRL approach significantly surpasses conventional DRL methods and random IRS configurations, delivering notable sum-rate gains and fast online inference (≈0.5 ms per decision). This approach enables practical, real-time IRS control in OWC systems, improving coverage and reliability with minimal additional power consumption.

Abstract

Intelligent reflecting surfaces (IRSs) have emerged as a promising solution to mitigate line-of-sight (LoS) blockages and enhance signal coverage in optical wireless communication (OWC) systems with minimal additional power. In this work, we consider a mirror-based IRS to assist a dynamic indoor visible light communication (VLC) environment. We formulate an optimization problem that aims to maximize the sum rate by adjusting the orientation of the IRS mirrors. To enable real-time adaptability, the problem is modelled as a Markov decision process (MDP), and a deep reinforcement learning (DRL) algorithm is developed based on the deterministic policy gradient for real-time mirror-based IRS optimization in dynamic VLC networks. The proposed DRL is employed to optimize mirror orientation toward mobile users under blockage and mobility constraints. Simulation results demonstrate that our proposed DRL algorithm outperforms the conventional deep Q- learning (DQL) algorithm and achieves substantial improvements in sum rate compared to random-orientation IRS configurations

Adaptive DRL for IRS Mirror Orientation in Dynamic OWC Networks

TL;DR

This work tackles dynamic indoor VLC with LoS blockages by employing a mirror-based IRS to steer reflected signals toward mobile users. It formulates the problem as an MDP and develops a DRL algorithm based on deterministic policy gradients to continuously orient IRS mirrors for multiple users under blockage and QoS constraints. The results show the proposed DRL approach significantly surpasses conventional DRL methods and random IRS configurations, delivering notable sum-rate gains and fast online inference (≈0.5 ms per decision). This approach enables practical, real-time IRS control in OWC systems, improving coverage and reliability with minimal additional power consumption.

Abstract

Intelligent reflecting surfaces (IRSs) have emerged as a promising solution to mitigate line-of-sight (LoS) blockages and enhance signal coverage in optical wireless communication (OWC) systems with minimal additional power. In this work, we consider a mirror-based IRS to assist a dynamic indoor visible light communication (VLC) environment. We formulate an optimization problem that aims to maximize the sum rate by adjusting the orientation of the IRS mirrors. To enable real-time adaptability, the problem is modelled as a Markov decision process (MDP), and a deep reinforcement learning (DRL) algorithm is developed based on the deterministic policy gradient for real-time mirror-based IRS optimization in dynamic VLC networks. The proposed DRL is employed to optimize mirror orientation toward mobile users under blockage and mobility constraints. Simulation results demonstrate that our proposed DRL algorithm outperforms the conventional deep Q- learning (DQL) algorithm and achieves substantial improvements in sum rate compared to random-orientation IRS configurations
Paper Structure (11 sections, 15 equations, 5 figures, 1 table, 1 algorithm)

This paper contains 11 sections, 15 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. VLC channel model
  4. User Mobility and Blockage
  5. User data rate
  6. Problem Formulation
  7. Intelligent RL solution based on DRL
  8. performance evaluation
  9. Simulation configuration
  10. Simulation Results
  11. Conclusion

Figures (5)

  • Figure 1: IRS-assisted VLC system model with the DRL framework.
  • Figure 2: Blockage scenario.
  • Figure 3: Sum rates versus iterations. $P=2$ W.
  • Figure 4: Sum rates versus the transmitted optical power considering different numbers of blockages. IRS = 100.
  • Figure 5: Bit error rates versus SNR.