CSI-Based MIMO Indoor Positioning Using Attention-Aided Deep Learning

Location-based services have become an indispensable component of wireless networks, but high-precision positioning is challenging. With the application of multiple-input multiple-output (MIMO) in 5G, accurate channel state information (CSI) can be obtained and leveraged for high-precision positioning. Solving the MIMO positioning problem by deep learning has demonstrated better accuracy than traditional methods. To further improve the positioning accuracy, we propose a novel deep learning model named ACPNet, which incorporates two types of attention mechanisms and an improved training scheme. Experiment results show that compared to the state-of-the-art work, ACPNet exhibits more than 20% positioning accuracy improvement, and also maintains a relatively low computation complexity.