A Deep Learning-Based Indoor Positioning Approach Using Channel and Spatial Attention

Abstract

The emerging B5G and 6G applications have brought forth the need for high-precision indoor localization. However, the complexity of indoor environments poses significant challenges to this goal, particularly due to the presence of non-line-of-sight (NLOS) conditions and multipath effects. This letter proposes an attention-based positioning network (ABPN) that exploits fine-grained features from MIMO channel state information (CSI) by spatial attention to combat the limited receptive field of traditional convolutional neural networks (CNNs) as well as channel attention to discriminate the importance of different wireless channels. Extensive experiments, conducted on two real-world datasets, demonstrate that the proposed ABPN outperforms the popular PirnatEco, AAresCNN, MIMOnet and CLnet with an average localization accuracy improvement of over 50%.