Modulated Autocorrelation Convolution Networks for Automatic Modulation Classification Based on Small Sample Set
Duona Zhang, Wenrui Ding, Chunhui Liu, Hongyu Wang, Baochang Zhang · IEEE Access · 2020
For modulation classification, hand-crafted approaches can generalize well from a few samples, yet deep learning algorithms require millions of samples to achieve the superior performance with purely data-driven manner. However for many practical problems only with small sample set (SSS) available, there still remains a challenge for deep learning. In this paper, we employ deep learning to solve the modulation classification task in a more practical setting, particularly suffering from the SSS problem and with low signal-to-noise ratios (SNRs). Novel modulated autocorrelation convolution networks (MACNs) are introduced to capture periodic representation for automatic modulation classification (AMC). In MACNs, modulated communication signals are classified with the periodic local features under an autocorrelation convolution criterion. Modulation filters are utilized to enhance the capacity of the convolution filters and compress the model. On a challenging SSS learning task in low SNRs, MACNs achieve state-of-the-art performance that outperforms the existing algorithms for AMC, while compressing the size of required storage space of convolutional filters by a factor of 8 compared with convolution neural networks (CNNs).