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This paper describes the architecture and performance of ORACLE, an approachfor detecting a unique radio from a large pool of bit-similar devices (samehardware, protocol, physical address, MAC ID) using only IQ samples at thephysical layer. ORACLE trains a convolutional neural network (CNN) thatbalances computational time and accuracy, showing 99\% classification accuracyfor a 16-node USRP X310 SDR testbed and an external database of $>$100 COTSWiFi devices. Our work makes the following contributions: (i) it studies thehardware-centric features within the transmitter chain that causes IQ samplevariations; (ii) for an idealized static channel environment, it proposes a CNNarchitecture requiring only raw IQ samples accessible at the front-end, withoutchannel estimation or prior knowledge of the communication protocol; (iii) fordynamic channels, it demonstrates a principled method of feedback-driventransmitter-side modifications that uses channel estimation at the receiver toincrease differentiability for the CNN classifier. The key innovation here isto intentionally introduce controlled imperfections on the transmitter sidethrough software directives, while minimizing the change in bit error rate.Unlike previous work that imposes constant environmental conditions, ORACLEadopts the `train once deploy anywhere' paradigm with near-perfect deviceclassification accuracy.

ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks