Multi-domain CSI-based Indoor Localization with Deep Attention Networks for MIMO JCAS system

Indoor localization is essential for future 6G systems that combine communication and sensing. Accurate positioning is vital for applications like augmented reality and autonomous robotics. In this context, we propose a multi-domain channel state information (CSI)-based localization approach using deep attention networks (DAN) in a massive multiple input multiple output-based (maMIMO) system. We introduce the extraction of features from CSI information in multiple domains, including time, frequency, and Doppler, and design uni-domain and multidomain feature sets. We implement the proposed DAN approach leveraging attention mechanisms to integrate and effectively process the multi-domain CSI data. We evaluate the performance of our model using a publicly available maMIMO dataset and compare it with baseline convolutional neural network (CNN) models. Our results indicate that the DAN-based approach enhances localization performance more than uni-domain, multidomain CNN models and also existing multi-domain-based CNN benchmarks. These findings highlight the benefits of using multidomain features, especially from the Doppler domain along with attention mechanisms for reliable indoor localization.