A trajectory prediction based intelligent handover control method in UAV cellular networks

The airborne base station (ABS) can provide wireless coverage to the ground in unmanned aerial vehicle (UAV) cellular networks. When mobile users move among adjacent ABSs, the measurement information reported by a single mobile user is used to trigger the handover mechanism. This handover mechanism lacks the consideration of movement state of mobile users and the location relationship between mobile users, which may lead to handover misjudgments and even communication interrupts. In this paper, we propose an intelligent handover control method in UAV cellular networks. Firstly, we introduce a deep learning model to predict the user trajectories. This prediction model learns the movement behavior of mobile users from the measurement information and analyzes the positional relations between mobile users such as avoiding collision and accommodating fellow pedestrians. Secondly, we propose a handover decision method, which can calculate the users' corresponding receiving power based on the predicted location and the characteristic of air-to-ground channel, to make handover decisions accurately. Finally, we use realistic data sets with thousands of non-linear trajectories to verify the basic functions and performance of our proposed intelligent handover control method. The simulation results show that the handover success rate of the proposed method is 8% higher than existing methods.