LAUSR

CSI-Based physical layer authentication is a promising candidate to achieve fast and lightweight authentication for wireless communication. However, the current methods usually cannot achieve initial authentication and are susceptible to channel noise. Besides, the current learning-aided physical layer authentication usually requires illegitimate channel state information (CSI) samples that are difficult to obtain. This letter proposes a newly deep-CSI-based authentication scheme to solve the above problems. We map CSI to a device’s location and further to its authenticated identity via deep learning in a static environment. Therefore, the proposed scheme does not require the cooperation of cryptography-based authentication to achieve initial authentication. The deep-learning-based authenticator with a confidence score branch is designed to learn the mapping relationship between the CSI and the identity. The confidence score branch can output a scalar that indicates whether the device is legitimate or not in the absence of illegitimate device CSI samples. CSI data are constructed as CSI images and implementation tricks are proposed to train the authenticator. Experiment results show that the authenticator performs well on all metrics and is robust to channel estimation errors.