Machine Learning Based Seamless Vertical Handoff Mechanism for Hybrid Li-Fi/Wi-Fi Networks

Abstract

Blockage of Visible Light Communication (VLC) links by mobile obstacles such as humans is one of the key problems to solve in order to achieve the wide-scale deployment of indoor VLC networks. In this work we present a solution to this problem by developing a predictive vertical handoff algorithm between Light Fidelity (Li-Fi) and Wireless Fidelity (Wi-Fi) networks. By using a state-of-the-art machine learning based forecasting model our handoff algorithm predicts the number of time intervals for which blockage will occur in the next time block. Based on this prediction our algorithm proactively hands off from Li-Fi to Wi-Fi and from Wi-Fi to Li-Fi in a manner that trades off the Average Available Data Rate (AADR) and the percentage service interruption. We demonstrate the performance of our algorithm on data collected in a life simulation environment in which humans move about in an indoor setting and block Li-Fi links. We show that our algorithm maintains a high AADR while achieving a very low percentage service interruption. Furthermore we show that by varying the values of the parameters of our algorithm we can achieve a gradual trade-off between AADR and the percentage service interruption. Our algorithm paves the way to high-performance hybrid Li-Fi/Wi-Fi networks that bear the potential to significantly change the landscape of indoor communication in the near future. © 2022 Elsevier B.V. All rights reserved.