Radio Modulation Classification Using Deep Residual Neural Networks
Radio Modulation Classification Using Deep Residual Neural Networks
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DOI：
10.1109/milcom55135.2022.10017640
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Adeeb Abbas;Vasil Pano;G. Mainland;K. Dandekar
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文献类型：
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作者：
Adeeb Abbas;Vasil Pano;G. Mainland;K. Dandekar
提出了一种新的用于自动调制分类的深度残差网络——OPResNet-18。它在RadioML 2016.10a数据集上达到了最先进的精度。我们通过添加载波频率偏移（CFO）来训练所提出的模型和其他最先进的网络。我们发现先前提出的IQNet-3对CFO具有鲁棒性。我们证明，与现有无法处理CFO的神经网络相比，这种鲁棒性允许IQNet-3的性能通过数据增强得到进一步改善。最后，我们提供的证据表明，据报道在许多领域表现良好的时域数据的标准数据预处理技术在IQ领域的表现不如简单的替代方案，即外部产品。
We propose a new deep residual network for Automatic Modulation Classification, OPResNet-18. It achieves state-of-the-art accuracy on the RadioML 2016.10a data set. We train the proposed model and other state-of-the-art networks with augmented data by adding a Carrier Frequency Offset (CFO). We find that the previously proposed IQNet-3 is robust to CFO. We demonstrate that this robustness allows the performance of IQNet-3 to be further improved through data augmentation in contrast to existing neural networks that cannot handle CFO. Finally, we provide evidence that standard data pre-processing techniques for time-domain data that reportedly perform well in many domains do not perform as well as a simple alternative, the outer product, in the IQ domain.