  Deep learning-enabled device fingerprinting has proven efficient in enabling
automated identification and authentication of transmitting devices. It does so
by leveraging the transmitters' unique features that are inherent to hardware
impairments caused during manufacturing to extract device-specific signatures
that can be exploited to uniquely distinguish and separate between (identical)
devices. Though shown to achieve promising performances, hardware
fingerprinting approaches are known to suffer greatly when the training data
and the testing data are generated under different channels conditions that
often change when time and/or location changes. To the best of our knowledge,
this work is the first to use MIMO diversity to mitigate the impact of channel
variability and provide a channel-resilient device identification over flat
fading channels. Specifically, we show that MIMO can increase the device
classification accuracy by up to about $50\%$ when model training and testing
are done over the same channel and by up to about $70\%$ when training and
testing are done over different fading channels.
