An Artificial Neural Network-Based Handover Scheme for Hybrid LiFi Networks

Abstract

Combining the ultra-high user throughput of the light fidelity (LiFi) and the ubiquitous coverage of wireless fidelity (WiFi), the hybrid LiFi and WiFi network (HLWNet) demonstrates unparalleled advantages in indoor wireless data transmission. Due to the line-of-sight propagation nature of the optical signal, the handover decision-making problem in HLWNets, however, becomes more critical and challenging than that in previous heterogeneous networks. In this paper, the handover decision-making problem in the HLWNet is regarded as a binary classification problem, and an artificial neural network (ANN)-based handover scheme is proposed. The complete handover scheme consists of two sets of ANNs that use the information about channel quality, user movement, and device orientation as input features to make handover decisions. After being trained with the labeled datasets that are generated with a novel approach, the ANN-based handover scheme is able to achieve over 95% handover accuracy. The proposed scheme is then compared with benchmarks under an indoor simulation scenario. The simulation results show that the proposed approach can significantly increase user throughput by 20.5 – 46.7% and reduce handover rate by around 59.5 – 78.2% as compared with the benchmarks; in the meanwhile, it maintains a great robustness performance against user mobility and channel variation.

Keywords

• Handover
• light fidelity (LiFi)
• machine learning (ML)
• sixth-generation wireless network (6G)
• visible light communication (VLC)
• wireless fidelity (WiFi)