Server-Controlled Intelligent Reflecting Surface Networks for Optimized Beam Routing

Abstract

Intelligent reflecting surfaces (IRS) have emerged as a promising solution to enhance signal propagation and mitigate interference in next-generation wireless networks. The purpose of this study is to develop and evaluate an optimized beam routing framework for IRS-assisted wireless communication in complex urban environments. To achieve this, this study proposes an IRS-assisted beam routing framework that leverages metaheuristic optimization to determine optimal signal paths in complex urban environments. The key contribution lies in the design of a server-controlled IRS network that dynamically adjusts phase shifts based on environmental factors and user locations, enabling adaptive and efficient signal routing. A server-controlled IRS network dynamically adjusts phase shifts to ensure efficient transmission, overcoming obstacles and improving network coverage. Extensive simulation results show that the proposed method significantly enhances signal reliability, reduces path loss, and outperforms existing IRS-based approaches in terms of path optimization and communication efficiency. Specifically, the proposed framework achieves a 40% improvement in path efficiency compared to prior state-of-the-art methods, as confirmed through comparative analysis. Comparative analysis with prior research confirms a 40% improvement in path efficiency, validating the system's effectiveness. These findings highlight the potential of IRS-based beamforming as a scalable and robust solution for sixth-generation (6G) wireless networks and beyond. Future work will focus on real-world validation and the integration of machine learning techniques for adaptive IRS configurations.