Joint Resource Slicing and Vehicle Association for Drone-Assisted Vehicular Networks

Abstract

The drone-small-cell-assisted air-ground integrated network is a promising architecture for enabling diverse vehicle applications. This paper presents a joint resource slicing and vehicle association framework for drone-assisted vehicular networks, which facilitates spectrum sharing among heterogeneous base stations (BSs) and achieves dynamic resource provisioning in the presence of network load dynamics. We formulate the network utility maximization problem as mixed-integer nonlinear programming, considering traffic statistics, quality-of-service (QoS) constraints, varying vehicle locations, load conditions in each cell, and interdrone interference. The original maximization problem is transformed into a biconcave optimization problem to ensure mathematical tractability. An alternate concave search algorithm is then designed to iteratively solve vehicle association patterns and spectrum partitioning among heterogeneous BSs until convergence. Simulation results show that the proposed scheme achieves a significant performance improvement in throughput and spectrum utilization compared with two other baseline schemes.