An Efficient DRL Based Technique for Resource Allocation in Heterogeneous WiFi/LiFi Systems

Wireless fidelity (WiFi) and light fidelity (LiFi) offer extensive coverage areas and high data rates, respectively. Each of them operate on distinct and non-interfering spectra. The integration of both these technologies creates a hybrid WiFi/LiFi network, capable of delivering high achievable sum rate (ASR) and enhanced network mobility. In hybrid WiFi/LiFi networks, resource allocation remains a significant challenge. This study focused on model free approach for resource allocation using deep reinforcement learning (DRL), which is simulated under realistic constraints such as signal blockage and load balancing. Simulations results show that the proposed DRL approach surpasses traditional deep Q-network (DQN)-based methods by 42.3% in terms of ASR with 35.4% lower transmission power usage, leading to better resource management and enhanced overall network performance.