  The accurate identification of wireless devices is critical for enabling
automated network access monitoring and authenticated data communication in
large-scale networks; e.g., IoT. RF fingerprinting has emerged as a solution
for device identification by leveraging the transmitter unique manufacturing
impairments. Although deep learning is proven efficient in classifying devices
based on the hardware impairments fingerprints, DL models perform poorly due to
channel variations. That is, although training and testing neural networks
using data generated during the same period achieve reliable classification,
testing them on data generated at different times degrades the accuracy
substantially, an already well recognized problem within the community. To the
best of our knowledge, we are the first to propose to leverage MIMO
capabilities to mitigate the channel effect and provide a channel-resilient
device classification. We show that for AWGN channels, combining multiple
received signals improves the testing accuracy by up to $30\%$. We also show
that for Rayleigh channels, blind channel estimation enabled by MIMO increases
the testing accuracy by up to $40\%$ when the models are trained and tested
over the same channel, and by up to $60\%$ when the models are tested on a
channel that is different from that used for training.
