A UAV communication strategy based on user distribution time series prediction

Abstract

Abstract To maximize the role of unmanned aerial vehicles (UAVs) as mobile relays to assist communication, while meeting the requirements of service quality and energy constraints, it is necessary to fully consider the motion trajectories and spatiotemporal distribution of ground users in UAV wireless communication systems. Therefore, an algorithm that combines Long Short-Term Memory (LSTM) and Gaussian Mixture Model (GMM) is proposed to predict user distribution. The LSTM, with its capability of handling temporal data, enables the UAV to refer to the preceding information during flight decision-making and make action outputs based on predictions of the dynamic environment. Simulation results demonstrate that the proposed approach can accurately predict the position information of mobile users, achieve faster convergence, and obtain better performance in dynamic scenarios. The system performance has improved by 5.8 percent.