  From the viewpoint of physical-layer authentication, spoofing attacks can be
foiled by checking channel state information (CSI). Existing CSI-based
authentication algorithms mostly require a deep knowledge of the channel to
deliver decent performance. In this paper, we investigate CSI-based
authenticators that can spare the effort to predetermine channel properties by
utilizing deep neural networks (DNNs). We first propose a convolutional neural
network (CNN)-enabled authenticator that is able to extract the local features
in CSI. Next, we employ the recurrent neural network (RNN) to capture the
dependencies between different frequencies in CSI. In addition, we propose to
use the convolutional recurrent neural network (CRNN)---a combination of the
CNN and the RNN---to learn local and contextual information in CSI for user
authentication. To effectively train these DNNs, one needs a large amount of
labeled channel records. However, it is often expensive to label large channel
observations in the presence of a spoofer. In view of this, we further study a
case in which only a small part of the the channel observations are labeled. To
handle it, we extend these DNNs-enabled approaches into semi-supervised ones.
This extension is based on a semi-supervised learning technique that employs
both the labeled and unlabeled data to train a DNN. To be specific, our
semi-supervised method begins by generating pseudo labels for the unlabeled
channel samples through implementing the K-means algorithm in a semi-supervised
manner. Subsequently, both the labeled and pseudo labeled data are exploited to
pre-train a DNN, which is then fine-tuned based on the labeled channel records.
