This paper describes the architecture and performance of ORACLE, an approach
for detecting a unique radio from a large pool of bit-similar devices (same
hardware, protocol, physical address, MAC ID) using only IQ samples at the
physical layer. ORACLE trains a convolutional neural network (CNN) that
balances computational time and accuracy, showing 99\% classification accuracy
for a 16-node USRP X310 SDR testbed and an external database of $>$100 COTS
WiFi devices. Our work makes the following contributions: (i) it studies the
hardware-centric features within the transmitter chain that causes IQ sample
variations; (ii) for an idealized static channel environment, it proposes a CNN
architecture requiring only raw IQ samples accessible at the front-end, without
channel estimation or prior knowledge of the communication protocol; (iii) for
dynamic channels, it demonstrates a principled method of feedback-driven
transmitter-side modifications that uses channel estimation at the receiver to
increase differentiability for the CNN classifier. The key innovation here is
to intentionally introduce controlled imperfections on the transmitter side
through software directives, while minimizing the change in bit error rate.
Unlike previous work that imposes constant environmental conditions, ORACLE
adopts the `train once deploy anywhere' paradigm with near-perfect device
classification accuracy.

ORACLE 是一种检测来自大量相似设备的独特无线电信号的方法，它利用卷积神经网络（CNN），能够平衡计算时间和准确性，而且只需物理层的 IQ 样本来训练。它通过软件指令有意引入控制性的不完美之处，以提高 CNN 的分类器的可区分度，而且呈现出 99％的分类精度。