Joint Resource Allocation and Trajectory Control for UAV-Enabled Vehicular Communications

• Main Authors: Lijun Deng, Gang Wu, Jingwei Fu, Yizhong Zhang, Yifu Yang
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Resource allocation; vehicular communications; UAV; graph theory; D2D; Q-learning
• Online Access: https://ieeexplore.ieee.org/document/8839771/

In this paper, aiming at the emergency coverage for vehicular network, we consider the problem of resource allocation for unmanned-aerial-vehicle (UAV) enabled vehicular communications, where UAV work as a temporary cellular base station. Our objective is to maximize the sum achievable rate of vehicle-to-infrastructure (V2I) communications and ensure the reliability of vehicle-to-vehicle (V2V) communications by dynamic coverage provided by UAV. Firstly, through theoretical analysis, optimal transmission power expressions for cellular users (CUEs) and device-to-device users (DUEs) are given, respectively. Secondly, by utilizing 3-partite graph matching and Hungarian algorithm, we present two graph-based methods for spectrum sharing and resource block assignment of UAV enabled vehicular network. Moreover, considering the mobility of UAV, we adopt the Q-Learning algorithm to control the trajectory of UAV in order to adapt to the time-varying channel. Finally, the feasibility of the presented schemes are verified by simulation and compared to randomized matching scheme. The simulation results show that the sum achievable rate of V2I links increases with the increase of the maximum transmission power of CUEs and the interruption probability of V2V links, and decreases with the increase of the ratio of DUEs to CUEs and the minimum capacity requirement of single V2I link. Moreover, the sum achievable rate of V2I links is enhanced by controlling the trajectory of UAV in real time.