Trajectory Data Driven V2V/V2I Mode Switching and Bandwidth Allocation for Vehicle Networks
Zhilong Zhang, Xuefei Li, Danpu Liu, Tao Luo, Yi Zhang
Abstract
Maintaining efficient and reliable communication services in vehicle networks is challenging due to the high mobility of vehicle users. One of the key issues is to deal with the large amount of channel feedback and make better use of limited wireless resources in real time. In this letter, we propose a trajectory data driven method to maximize the total user utilities, which is characterized by intelligent channel state information (CSI) feedback reduction and vehicle-to-vehicle (V2V)/vehicle-to-infrastructure (V2I) mode switching. A deep neural network (DNN) model is trained using optimization approaches and vehicles' trajectory data. Based on the well trained model, V2V/V2I mode is selected adaptively and bandwidth is allocated optimally with partial channel feedback. Simulation results show that our proposed method outperforms the comparison baseline and can achieve 96.23% of the performance obtained by exhausting searching with full CSI feedback.