Abstract
Positioning technology based on 5G networks has been deeply integrated into everyday life. Despite this, severe non-line-of-sight (NLOS) conditions in wireless signal environments can cause signal obstructions, negatively impacting the precision and dependability of positioning services. This paper introduces an innovative algorithm called Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface Non-Line-of-Sight Angle of Arrival (STAR-RIS NLOS AOA) to address these challenges. The algorithm initially develops a system model named 5G STAR-RIS localization (GSL). By integrating STAR-RIS into the system, the model effectively overcomes the challenges of positioning in NLOS scenarios. The inclusion of STAR-RIS not only boosts the system’s adaptability but also meets the positioning requirements for users on both sides of the reflective surface simultaneously. The algorithm then utilizes the Root-MUSIC algorithm for estimating user coordinates. An optimization problem is formulated based on these estimations, with the goal of reducing the gap between estimated and real coordinates. To address this optimization, the Inertia Weight Whale Optimization Algorithm is employed, providing high-precision estimations of users’ three-dimensional positions. Simulations reveal that the proposed Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface Non-Line-of-Sight Angle of Arrival (SRNA) algorithm substantially outperforms conventional algorithms in positioning performance across different signal-to-noise ratio contexts. Specifically, in challenging NLOS situations, the SRNA algorithm can cut positioning errors by 50% to 62%, demonstrating its outstanding capability and efficiency in addressing the difficulties presented by NLOS conditions within 5G-based positioning systems.