论文标题
带有合成训练数据及其现实性能的RF信号分类
RF Signal Classification with Synthetic Training Data and its Real-World Performance
论文作者
论文摘要
神经网是电磁频谱中无线电信号分类的强大方法。由于缺乏多样化和大量的实际RF数据，这些神经网通常经过合成生成的数据训练。但是，通常尚不清楚如何在现实世界应用中对合成数据进行训练的神经网。本文研究了在合成训练数据中建模的不同RF信号障碍（例如相，频率和样本率偏移，接收器过滤器，噪声和通道模型）的影响。为此，本文通过不同的信号障碍的各种合成训练数据集训练神经网。训练后，对现场定义的无线电接收器收集的现实世界RF数据评估了神经网。这种方法揭示了应包括在精心设计的合成数据集中的建模信号障碍。研究的显示示例可以将RF信号分类为短波频段的20种不同无线电信号类型之一。通过仅使用精心设计的合成训练数据，它在实际操作中可实现高达95％的精度。
Neural nets are a powerful method for the classification of radio signals in the electromagnetic spectrum. These neural nets are often trained with synthetically generated data due to the lack of diverse and plentiful real RF data. However, it is often unclear how neural nets trained on synthetic data perform in real-world applications. This paper investigates the impact of different RF signal impairments (such as phase, frequency and sample rate offsets, receiver filters, noise and channel models) modeled in synthetic training data with respect to the real-world performance. For that purpose, this paper trains neural nets with various synthetic training datasets with different signal impairments. After training, the neural nets are evaluated against real-world RF data collected by a software defined radio receiver in the field. This approach reveals which modeled signal impairments should be included in carefully designed synthetic datasets. The investigated showcase example can classify RF signals into one of 20 different radio signal types from the shortwave bands. It achieves an accuracy of up to 95 % in real-world operation by using carefully designed synthetic training data only.
