UAV-Assisted Vehicular Communication System Optimization With Aerial Base Station and Intelligent Reflecting Surface
Peng Wang, Demin Li, Yihong Zhang, Xuemin Chen
Abstract
Unmanned aerial vehicle (UAV) has recently contributed significantly to vehicle communications, offering promising solutions for enhancing connectivity in modern transportation systems. However, ensuring reliable signal coverage of the UAV and overcoming obstacles that hinder direct communication between vehicles remain challenging. To address these issues, this paper explores the use of the UAV as an aerial base station (ABS) and intelligent reflecting surface (IRS) for enhancing vehicular communication networks. The goal is to maximize the average bit rate of vehicles in both UAV-to-vehicle (U2V) and vehicle-to-vehicle (V2V) communication while ensuring minimum communication rate requirements. To remedy the problem which is formulated as a complex and non-convex optimization, a novel average bit rate algorithm based on alternative optimization (AO) technique is applied. Then a low-complexity linear relaxation method is proposed for scheduling variables, and a closed-form solution to the problem of IRS phase is presented. The trajectory optimization problem is transformed into an approximate convex problem with the help of successive convex approximation (SCA) method. The paper concludes with the proof of convergence and numerical results, demonstrating that our proposed design exhibits better performance than those corresponding baseline schemes.