论文标题
神经网络的端到端无线电指纹
End-to-End Radio Fingerprinting with Neural Networks
论文作者
论文摘要
本文提出了一种从传输信号分类射频（RF）设备的新方法。给定来自相同设备的信号集合，我们将传输的距离和特定设备身份的距离进行准确分类。我们开发了一个多个分类器系统，该系统可以准确区分通道，并使用归一化的同相和正交（IQ）样本对设备进行分类。我们的网络使用剩余连接进行距离和设备分类，达到88.33％的精度，在11个不同的距离和两个不同时间的16个独特设备上，在以前无法实现的任务上。此外，我们证明了对大规模数据域和微妙分类差异的预训练神经网络的功效。
This paper presents a novel method for classifying radio frequency (RF) devices from their transmission signals. Given a collection of signals from identical devices, we accurately classify both the distance of the transmission and the specific device identity. We develop a multiple classifier system that accurately discriminates between channels and classifies devices using normalized in-phase and quadrature (IQ) samples. Our network uses residual connections for both distance and device classification, reaching 88.33% accuracy classifying 16 unique devices over 11 different distances and two different times, on a task that was previously unlearnable. Furthermore, we demonstrate the efficacy for pre-training neural networks for massive data domains and subtle classification differences.
