Enhancing Hybrid System Based on Reinforcement Learning

Abstract

Light fidelity (Li-Fi) can be defined as a type of wireless technology that sends data via light waves through LED light bulbs. Light fidelity (Li-Fi) offers high-speed data transmission capabilities and a large unlicensed bandwidth, making it a promising technology for the future. However, factors including interference, wall reflection, and blocking may cause the quality of a light fidelity (Li-Fi) channel to vary from one part of a room to another. Another type of wireless communication technology that offers broad coverage and slow transmission rates is wireless fidelity (Wi-Fi). Since the electromagnetic spectrums regarding such two technologies do not overlap, there is a possibility for building a hybrid Wi-Fi and Li-Fi network that provides seamless and high throughput global communication. One wireless fidelity access point (Wi-Fi AP) and four light fidelity access points (Li-Fi Aps) make up the downlink hybrid system we discuss in this work. Finding an access point (AP) assignment method that will increase long-term system throughput at the same time as still guaranteeing users' pleasure and fairness is challenging. Thus, the authors suggest using a reinforcement learning (RL) algorithm. The algorithm aims to balance the load of multiple access points (APs) by considering both the LiFi and Wi-Fi channels. The results obtained using MATLAB code for a hybrid system based on reinforcement learning (RL) and a standard solution set size (SSS) access technology called TDMA. The system was evaluated in terms of throughput and user satisfaction for 5 users. According to the results, the RL-based hybrid system achieved a throughput of up to 210 Mbps and an SSS of 180 Mbps. Additionally, the user satisfaction was reported to be 100%.