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Deep learning-enabled device fingerprinting has proven efficient in enablingautomated identification and authentication of transmitting devices. It does soby leveraging the transmitters' unique features that are inherent to hardwareimpairments caused during manufacturing to extract device-specific signaturesthat can be exploited to uniquely distinguish and separate between (identical)devices. Though shown to achieve promising performances, hardwarefingerprinting approaches are known to suffer greatly when the training dataand the testing data are generated under different channels conditions thatoften 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 channelvariability and provide a channel-resilient device identification over flatfading channels. Specifically, we show that MIMO can increase the deviceclassification accuracy by up to about $50\%$ when model training and testingare done over the same channel and by up to about $70\%$ when training andtesting are done over different fading channels.

Deep Learning-Enabled Zero-Touch Device Identification: Mitigating the Impact of Channel Variability Through MIMO Diversity