More Is Better: Data Augmentation for Channel-Resilient RF Fingerprinting
More Is Better: Data Augmentation for Channel-Resilient RF Fingerprinting
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DOI：
10.1109/mcom.001.2000180
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发表时间：
2020-10-01
影响因子：
11.2
通讯作者：
Chowdhury, Kaushik
中科院分区：
文献类型：
--
作者：
Soltani, Nasim;Sankhe, Kunal;Chowdhury, Kaushik
RF指纹识别涉及识别无线信号内的特征发射机施加的变化。不依赖手工特征的深度神经网络（DNN）已被证明在指纹识别任务中非常有效，只要通道保持不变。然而，在特定位置和时间训练的DNN在不同信道条件下收集的数据集上表现不佳。本文提出了训练管道中的数据增强步骤，该步骤将DNN暴露于原始数据集中不存在的许多模拟通道和噪声变化。我们描述了两种数据增强的方法。当发送器侧数据（即，没有信道失真的纯信号）是可用的。第二种方法应用于“接收器数据”，当只有被动数据集可用于已经通过空中传输的信号时。我们表明，与非增强数据馈送到DNN的情况相比，数据增强在前一种情况下使用自定义生成的数据集可以提高75%，在后一种情况下使用5000个设备的WiFi数据集可以提高32- 51%。
RF fingerprinting involves identifying characteristic transmitter-imposed variations within a wireless signal. Deep neural networks (DNNs) that do not rely on handcrafting features have proven to be remarkably effective in fingerprinting tasks, as long as the channel remains invariant. However, DNNs trained at a specific location and time perform poorly on datasets collected under different channel conditions. This article proposes a data augmentation step within the training pipeline that exposes the DNN to many simulated channel and noise variations that are not present in the original dataset. We describe two approaches for data augmentation. The first approach is applied to the "transmitter data" when transmitter side data (i.e., pure signals without channel distortion) is available. The second approach is applied to the " receiver data" when only a passive dataset is available with already over-the-air transmitted signals. We show that data augmentation results in 75 percent improvement in the former case with a custom-generated dataset, and around 32-51 percent improvement in the latter case on a 5000-device WiFi dataset, compared to the case of non-augmented data fed to DNNs.