DRL-Driven Optimal User Association and Load Balancing in Hybrid RF/LiFi Based IoT Systems

The proliferation of Internet of Things (IoT) devices has raised considerable difficulties in the identification of users, optimization of power, and load balancing in hybrid RF/LiFi networks. As interconnection among devices increases, ensuring optimal performance while managing network resources efficiently becomes quite complex. This complexity arises due to accommodating the possibly diverse users’ needs, fluctuating channel conditions, and varying interference levels, all necessitating sophisticated management solutions to provide seamless connectivity and dependable communication. To tackle these issues, a deep joint hybrid system (DJHS) technique is presented, which employs proximal policy optimization (PPO), a cutting-edge deep reinforcement learning (DRL) algorithm. DJHS aims to effectively handle the intricate problems surrounding user association and load balancing while optimizing power usage in dynamic contexts. DJHS continuously updates its approach based on real-time network data through adaptive learning methods, allowing it to make intelligent decisions that improve overall system performance regarding data throughput and power optimization. Simulation results demonstrate that DJHS outperforms existing approaches such as sac, a2c, td3, and trpo regarding crucial metrics, including data rate and power transmission. Notably, DJHS’s ability to adjust to variations in signal-to-interference-plus-noise ratio (SINR) allows for enhanced resource allocation and network stability. This flexibility ensures that users receive optimal service even in changing conditions, enhancing the overall user experience.