Load Balancing of Hybrid LiFi WiFi Networks Using Reinforcement learning

Abstract

Light fidelity (LiFi) is an emerging communication technology that utilizes light intensity modulation in order to transfer data from light-emitting diode (LED) to users. Due to the vast visible light spectrum, LiFi can support high data rates; however, its coverage is limited. In contrast to LiFi, WiFi works in radio frequency and is capable of providing ubiquitous coverage with limited data rates. Since the spectrum of LiFi does not overlap with WiFi, both can co-exist to form a hybrid LiFi and WiFi network. The advantage of hybrid LiFi and WiFi network is that it provides high data rates and better connectivity. The performance of a hybrid LiFi and WiFi network significantly depends upon the load balancing strategies. Therefore, in this paper, gradient descent-based reinforcement learning (RL) has been proposed to determine an optimal access point (AP) assignment policy that aims to maximize the average network throughput while ensuring user’s satisfaction. The performance of the proposed method is then compared against conventional signal strength strategy (SSS); the results are presented in terms of the average network throughput, user satisfaction, and outage probability. Based on the results, it was observed that the proposed RL method provides a significant improvement in all the performance metrics over the SSS based method.