Location estimation for supporting adaptive beamforming

Abstract

This study presents a machine learning (ML)-based localization method for improving location estimation accuracy in wireless networks, especially in challenging environments where traditional techniques often fall short. Conventional methods rely on a limited number of multipath components (MPCs), leading to inaccurate localization in complex environments. By leveraging a novel dataset generated from ray-tracing simulations in urban and campus environments, we propose a deep neural network (DNN)-based method that incorporates rich channel metrics such as angle of arrival (AoA), time of arrival (ToA), and received signal strength (RSS). The DNN is trained on diverse scenarios, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, and outperforms traditional MPC-based methods, reducing localization error by up to 20%. Our approach challenges the conventional use of only 3 MPCs for localization and demonstrates that a larger number of MPCs enhances accuracy, particularly in urban and obstructed environments. This research provides important insights into the potential of ML-driven solutions for improving localization accuracy in next-generation wireless systems, such as 5G and beyond.