Optimized Beamforming and Bandwidth Allocation in Multi-Antenna UAV-Assisted Vehicular Networks

Abstract

Ensuring reliable communication for mission-critical vehicles in dynamic environments with limited infrastructure is a significant challenge due to interference and spectrum scarcity. This paper investigates a UAV-assisted vehicular communication framework that leverages multi-antenna beamforming and dynamic bandwidth allocation to provide prioritized and interference-mitigated wireless links. Vehicles are classified according to their service priority, with each class assigned a distinct frequency band to reduce interference. Within each class, optimized beamforming further minimizes transmission overlap and enhances spectral efficiency. The optimization problem is solved using an alternating optimization framework, incorporating two beamforming strategies: one based on successive convex approximation (SCA) and the other derived in closed form. Numerical results indicate that the proposed scheme outperforms baseline approaches that optimize only bandwidth allocation or beamforming in terms of overall system performance. Among the two joint optimization methods, the closed-form solution achieves higher sum rates and generally requires less transmit power, while also exhibiting lower computational complexity compared with the SCA-based approach.