Joint Optimization of Precoding, Phase Shift, and UAV Deployment for IRS-MIMO Air-to-Ground Integrated Vehicular Networks

Abstract

Deploying uncrewed aerial vehicles (UAVs) in vehicular networks overcomes the inherent limitations of terrestrial infrastructure by dynamically enhancing coverage and line-of-sight (LoS) connectivity. However, further enhancement of the data rate necessitates the deployment of additional terrestrial base stations (BSs) and UAVs, which incur high costs and consume a large amount of energy. To address these challenges, synergistically integrating UAVs equipped with intelligent reflecting surfaces (IRS) and multiple-input multiple-output (MIMO) techniques can effectively improve the channel capacity of air-to-ground integrated vehicular networks, while maintaining low cost and low power consumption. The key idea is to partially replace some of the required terrestrial relays with high-flexibility and energy-efficient UAVs equipped with IRS. Specifically, in IRS-MIMO air-to-ground integrated vehicular networks, we formulate a joint precoding design, phase shift optimization, and UAV deployment problem with the objective of maximizing the weighted sum-rate. To tackle this non-convex problem, an iterative optimization algorithm with polynomial-time complexity is developed, which enables a gradual approximation of a feasible solution to the formulated problem. Finally, simulation results demonstrate that the proposed scheme is superior to the state-of-the-art schemes in terms of the weighted sum-rate. Additionally, the impact of network parameters on transmission performance and the convergence of iterative optimization are thoroughly analyzed.