Simulation study demonstrates superior sum-rate and reduced power usage using deep reinforcement learning in hybrid WiFi/LiFi networks, indicating enhanced efficiency under mobile conditions.
Light fidelity (LiFi) and wireless fidelity (WiFi) offer high data rate and extensive coverage area, respectively. Each of them operate on different and non-interfering spectra. Integration of both these technologies creates a hybrid WiFi/LiFi network, which can provide high achievable sum rate with improved network mobility. In hybrid WiFi/LiFi networks, resource allocation remains a significant challenge. In this study, we propose a model-free deep reinforcement learning (DRL) method for resource allocation, which is simulated under realistic conditions, including signal blockage, load balancing and user equipment (UE) mobility. Simulations results show that the proposed DRL approach surpasses traditional Deep Q-Network (DQN)-based methods by 52.5% in terms of achievable sum-rate with 12.5% lower transmission power usage, leading to better resource management and enhanced overall network performance.
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Verma et al. (2024) studied this question.
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