Indoor Positioning with Multi-domain CSI-based Deep Attention Networks for MIMO Wireless Systems

Accurate indoor positioning is vital for applications such as augmented reality and autonomous robotics. Channel state information (CSI)-based methods, particularly when combined with beamforming, massive multiple input multiple output (mMIMO) techniques, and artificial intelligence (AI) algorithms, offer enhanced indoor user equipment (UE) positioning accuracy and robustness in complex indoor environments. In this paper, we present an AI-driven CSI-based indoor positioning method for mMIMO systems, where time, frequency, and Doppler features are extracted from the CSI data and combined to form both uni-domain and multi-domain feature sets. We introduce a deep attention network (DAN), an AI algorithm that leverages attention mechanisms to effectively integrate and process multi-domain CSI data for enhanced UE positioning performance. We evaluate DAN using a publicly available mMIMO dataset and compare its performance against the baseline and multi-domain convolutional neural network (CNN) models. Our results show that multi-domain DAN outperforms CNN approaches in positioning accuracy, though at the cost of increased inference complexity—highlighting a trade-off between performance and computational overhead. These findings demonstrate the potential of attention mechanisms and multi-domain CSI features for accurate indoor UE positioning systems.