Machine Learning-Driven User Localization in RIS-Assisted Wireless Systems
Abstract
The sixth generation (6G) targets ultra reliable, low latency (URLLC) gigabit connectivity in mmWave bands, where directional channels require precise beam alignment. Reconfigurable intelligent surfaces (RIS) reshape wave propagation and extend coverage, but they enlarge the beam search space at the base station, making exhaustive sweeps inefficient due to control overhead and latency. We propose an ML based user localization framework for RIS assisted communication at 27 GHz. A 20x20 RIS reflects signals from a core network connected base station and sweeps beams across the 0-90 degree elevation plane, divided into four angular sectors. We build a dataset by recording received signal power (Pr in dBm) across user locations and train multiple regressors, including decision tree (DT), support vector regressor (SVR), k nearest neighbor (KNN), XGBoost, gradient boosting, and random forest. In operation, an unknown user in the same plane measures four received power values (one per sector) and reports them to the pretrained RIS controller, which predicts the user's angular position in real time. Evaluation using mean absolute error (MAE), root mean squared error (RMSE), and R squared (R2) shows high accuracy. The DT model achieves an MAE of 4.8 degrees with R2 = 0.96, while other models reach 70 to 86 percent. Predicted radiation patterns, including main lobe alignment between 52 and 55 degrees, closely track ground truth. The framework reduces beam probing, enables faster alignment, and lowers latency for RIS assisted 6G networks.