A novel machine learning-based handover scheme for hybrid LiFi and WiFi networks

Conference item

Combining the high area spectrum efficiency of light fidelity (LiFi) and the ubiquitous coverage of wireless fidelity (WiFi), hybrid LiFi and WiFi networks have drawn increasing research attention. Meanwhile, the handover issue in hybrid networks becomes a hotspot since the coverage areas of LiFi and WiFi overlap each other. In addition, LiFi may cause frequent handovers for fast-moving users, while WiFi is susceptible to traffic overload. Consequently, the selection between LiFi and WiFi becomes a tricky problem. In this paper we propose a novel handover scheme, which adopts a dynamic coefficient via machine learning to adjust the selection preference between LiFi and WiFi. The new method balances channel quality, resource availability and user mobility to make handover decisions. Results show that compared to the received signal strength (RSS)-based and trajectory-based handover methods, the proposed scheme can improve the user’s throughput by up to 260% and 50%, respectively.

Authors: Wu, X; O'Brien, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Publication date: 2021-03-05
• Acceptance date: 2020-09-19
• Event title: IEEE Global Communications Conference Workshop 2020
• Event location: Online
• Event website: https://globecom2020.ieee-globecom.org/program/workshops
• Event start date: 2020-12-07
• Event end date: 2020-12-11
• DOI: 10.1109/GCWkshps50303.2020.9367577
• EISBN: 978-1-7281-7307-8
• ISBN: 978-1-7281-7308-5
• Language: English
• Keywords: artificial neural network; handover; machine learning; hybrid network; optical wireless communications (OWC); light fidelity (LiFi); FFR