Multi-IRS-Aided Localization for Next-Generation Wireless Networks in Fading Environments

Abstract

The growing demand for location-based services (LBS) in complex environments has increased the need for precise and reliable user localization techniques. Traditional methods often face limitations in scenarios with few access points (APs) and non-line-of-sight (NLOS) propagation, resulting in reduced accuracy. This paper presents a novel localization framework that leverages multiple Intelligent Reflecting Surfaces (IRS) to address these challenges and improve positioning accuracy in constrained conditions. The proposed method employs multiple IRSs to enhance signal propagation, mitigating the effects of NLOS conditions and improving signal quality. A Maximum Likelihood Estimation (MLE) algorithm is used to estimate user positions, while the Cramér-Rao Lower Bound (CRLB) is derived to benchmark the theoretical accuracy. By utilizing the reconfigurable capabilities of IRSs, the system dynamically adjusts wireless channels to optimize localization performance. Performance evaluations under practical fading conditions demonstrate significant improvements in accuracy compared to traditional methods. The results highlight the effectiveness and robustness of the proposed framework in diverse environments, showcasing the potential of IRS technology for advanced localization applications.