High-accuracy AoA-based Localization using Hierarchical ML Classifiers in Outdoor Environments

Accurate and reliable localization is a key requirement for 6G network operations, but it can be particularly challenging in outdoor environments. In this paper, we propose a machine learning (ML)–based localization framework that leverages angle of arrival (AoA) as a feature extracted from channel state information (CSI). The proposed approach employs high-resolution AoA estimation algorithms, including multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT), which feed a hierarchical, two-stage classifier to identify specific trajectories (hereafter referred to as tracks) in a given outdoor environment. The first stage of the classifier is a binary line-of-sight (LoS) / non-line-of-sight (NLoS) classifier, followed by region-specific multi-class classifiers for fine-grained identification of the specific LoS or NLoS tracks. We evaluate our approach using a real-world massive multiple-input multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) outdoor CSI dataset collected at the Nokia campus in Stuttgart, Germany. Experimental results show that i) the LoS / NLoS identification accuracy can reach 100%, and ii) the proposed two-stage approach significantly outperforms a single-stage multi-class baseline, achieving accuracy over 98% in LoS regions and 95% in NLoS regions. These findings demonstrate the potential of combining AoA with ML for robust localization in outdoor mMIMO propagation environments.