基于卷积神经网络特征的截断高斯判别分析的无线电信号分类
Classification of Radio Signals Using Truncated Gaussian Discriminant Analysis of Convolutional Neural Network-Derived Features
摘要：
为了提高分布式射频（RF）传感器和通信网络的实用性和可扩展性，减少对卷积神经网络（CNN）再训练的需求，并有效地共享有关信号的学习信息，我们研究了一种用于RF调制分类的监督自举方法。我们表明，CNN引导功能的新的和现有的调制类可以被认为是截断高斯分布的混合物，允许最大似然为基础的分类新的类，而无需重新训练的网络。在这项工作中，作者观察到使用CNN引导特征的最大似然估计的分类性能与在所有类别上训练的CNN的分类性能相当，即使对于那些没有训练引导CNN的类别。实现这一性能的同时，将新类定义所需的参数数量从800多万减少到仅200个。此外，在训练期间未直接标记的一些感兴趣的物理特征，例如信噪比（SNR），可以从这些相同的CNN导出的特征中学习或估计。最后，我们表明，SNR估计精度是最高的分类精度是最低的，因此可以用来校准的分类的信心。
英文摘要：
To improve the utility and scalability of distributed radio frequency (RF) sensor and communication networks, reduce the need for convolutional neural network (CNN) retraining, and efficiently share learned information about signals, we examined a supervised bootstrapping approach for RF modulation classification. We show that CNN-bootstrapped features of new and existing modulation classes can be considered as mixtures of truncated Gaussian distributions, allowing for maximumlikelihood-based classification of new classes without retraining the network. In this work, the authors observed classification performance using maximum likelihood estimation of CNNbootstrapped features to be comparable to that of a CNN trained on all classes, even for those classes on which the bootstrapping CNN was not trained. This performance was achieved while reducing the number of parameters needed for new class definition from over 8 million to only 200. Furthermore, some physical features of interest, not directly labeled during training, e.g. signal-to-noise ratio (SNR), can be learned or estimated from these same CNN-derived features. Finally, we show that SNR estimation accuracy is highest when classification accuracy is lowest and therefore can be used to calibrate a confidence in the classification.
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