We study the problem of interference source identification, through the lens of recognizing one of 15 different channels that belong to 3 different wireless technologies: Bluetooth, Zigbee, and WiFi. We employ deep learning algorithms trained on received samples taken from a 10 MHz band in the 2.4 GHz ISM Band. We obtain a classification accuracy of around 89.5% using any of four different deep neural network architectures: CNN, ResNet, CLDNN, and LSTM, which demonstrate the generality of the effectiveness of deep learning at the considered task. Interestingly, our proposed CNN architecture requires approximately 60% of the training time required by the state of the art while achieving slightly larger classification accuracy. We then focus on the CNN architecture and further optimize its training time while incurring minimal loss in classification accuracy using three different approaches: 1- Band Selection, where we only use samples belonging to the lower and uppermost 2 MHz bands, 2- SNR Selection, where we only use training samples belonging to a single SNR value, and 3- Sample Selection, where we try various sub-Nyquist sampling methods to select the subset of samples most relevant to the classification task. Our results confirm the feasibility of fast deep learning for wireless interference identification, by showing that the training time can be reduced by as much as 30x with minimal loss in accuracy.
Wavelet Transform Analytics for RF-Based UAV Detection and Identification System Using Machine Learning
在这项工作中，我们通过使用机器学习算法和预先训练的基于卷积神经网络的算法SqueezeNet作为分类器，对无线干扰（如WiFi和蓝牙）下基于射频（RF）的无人机检测和识别系统（DDI）进行了全面的比较分析。在射频信号指纹识别研究中，信号的瞬态和稳态可以用来从射频信号中提取唯一的签名。通过利用无人机（UAV）的射频控制信号进行DDI，我们分别考虑了信号的每个状态进行特征提取，并比较了无人机检测和识别的利弊。使用各种小波变换（离散小波变换、连续小波变换和小波散射变换）从信号中提取特征，我们使用这些特征建立不同的模型。我们研究了这些模型在不同信噪比（SNR）水平下的性能。通过使用小波散射变换从30 dB SNR下的RF信号的稳态中提取特征（散点图），并使用这些散点图来训练SqueezeNet，我们在10 dB SNR下实现了98.9%的准确度。
