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Radio frequency fingerprinting has been proposed for device identification.However, experimental studies also demonstrated its sensitivity to deploymentchanges. Recent works have addressed channel impacts by developing robustalgorithms accounting for time and location variability, but the impacts ofreceiver impairments on transmitter fingerprints are yet to be solved. In thiswork, we investigat the receiver-agnostic transmitter fingerprinting problem,and propose a novel two-stage supervised learning framework (RXA) to addressit. In the first stage, our approach calibrates a receiver-agnostic transmitterfeature-extractor. We also propose two deep-learning approaches (SD-RXA andGAN-RXA) in this first stage to improve the receiver-agnostic property of theRXA framework. In the second stage, the calibrated feature-extractor isutilized to train a transmitter classifier with only one receiver. We evaluatethe proposed approaches on transmitter identification problem using alarge-scale WiFi dataset. We show that when a trained transmitter-classifier isdeployed on new receivers, the RXA framework can improve the classificationaccuracy by 19.5%, and the outlier detection rate by 10.0% compared to a naiveapproach without calibration. Moreover, GAN-RXA can further increase theclosed-set classification accuracy by 5.0%, and the outlier detection rate by7.5% compared to the RXA approach.

GAN-RXA: A Practical Scalable Solution to Receiver-Agnostic Transmitter Fingerprinting