Real-time multi-IRS partitioning for sum-rate optimization in a UAV-IRS-aided vehicular communication system

Abstract

The rise of autonomous vehicles and intelligent transportation systems demands robust Vehicle-to-Everything (V2X) communication. However, the dynamic and unpredictable nature of vehicular environments, characterized by high mobility and complex signal propagation, presents significant challenges for reliable V2X operation. This paper addresses these challenges by proposing a novel communication model for dynamic vehicular networks that leverages multiple Unmanned Aerial Vehicles (UAVs) equipped with Intelligent Reflecting Surfaces (IRSs). The proposed framework optimizes reflections and trajectories to enhance signal quality and throughput in multi-vehicle environments. To overcome challenges in large-scale IRSs deployments, we introduce a two-step real-time multi-IRSs partitioning algorithm (RTMIP). We formulate an optimization problem to maximize the time-averaged sum-rate by jointly optimizing IRS reflection phases, IRS partitioning scheduling variables, and UAV trajectories, and design an alternating optimization algorithm to solve this problem. Simulation results demonstrate that the proposed algorithm significantly outperforms benchmark strategies in maximizing the time-averaged sum-rate in vehicular communications.