Joint Precoding for Hybrid RIS-assisted V2X Systems

The rapid expansion of urban Intelligent Transportation Systems (ITS) and the increasing deployment of Intelligent Connected Vehicles (ICVs) have contributed to a decrease in Vehicle-to-Everything (V2X) system capacity. To address this, we propose a hybrid Reconfigurable Intelligent Surface (RIS)-assisted V2X system that integrates both active and passive RIS elements. While active RIS elements with power amplifiers (PAs) mitigate path loss, they are costly. To achieve rate maximization with reduced complexity, we introduce a joint optimization algorithm that alternates between optimizing the active element selection matrix using a Tabu Search-based method and the precoding design, employing a neighborhood extraction based cross-entropy approach. Simulation results demonstrate that, under limited power constraints, our proposed algorithm significantly enhances system capacity compared to fixed active element schemes, effectively compensating for the reduced number of PAs.