Throughput and energy efficiency maximization for UAV-assisted vehicular networks

Abstract

Vehicle-to-Infrastructure (V2I) networks have a wide application prospect for providing vehicles with reliable road safety and infotainment services. This paper studies an Unmanned Aerial Vehicle (UAV)-assisted vehicular network, in which a UAV with cache is dispatched to communicate with moving vehicles and base stations. We study the throughput maximization problem by optimizing the power distribution and trajectory planning of the UAV subject to practical mobility constraints and the information-causality constraint. To solve the non-convex problem, we break it into two sub-problems and propose an iterative algorithm that jointly applying slack variables and sequential optimization. Due to the limited on-board energy of UAVs, improving the energy efficiency (EE) of the UAV is of great significance. To this end, we propose an efficient algorithm by merging fractional programming and sequential optimization. Moreover, we set two special schemes as benchmarks to measure the performance of the proposed algorithms. Numerical results show that the application of cache and the proposed algorithms notably enhance the throughput and EE of the UAV-assisted V2I network.