LAUSR

Automatic modulation classification (AMC) aims to automatically identify the modulation type of a detected signal in an intelligent wireless receiver, such as software-defined radio (SDR). Recently, deep learning-based methods such as convolutional neural networks have been applied to AMC, showing high-accuracy performance. However, the earlier studies do not consider various signal degradations that can possibly occur during the transmission and reception of wireless signals. Particularly, the signal reception can be often unstable, and the signal can be partially received due to the dynamic spectrum sensing or signal sensing in the intelligent wireless systems. The corrupted signal with missing samples considerably degrades the accuracy of modulation classification of the deep learning-based models, because it is very different from the training datasets. To address this issue, the preprocessing process of restoring the corrupted signal, called signal inpainting, is essential. Although it is significant for the modulation classification, no studies have been performed to investigate the effect of signal inpainting on AMC. To that end, this study proposes a generative adversarial network(GAN)-based signal inpainting method that fills in the missing samples in a wireless signal. The proposed inpainting method can restore the time-domain signal with up to 50% missing samples while maintaining the global structure of each modulation type. The correct recovery of the global structure enables the extraction of distinctive features that play a key role in the modulation classification. To investigate this effect of signal inpainting on AMC, we perform intensive experiments on the RadioML dataset that has been widely used in the AMC studies. We compare the accuracy performance of the two state-of-the-art AMC models with and without the proposed signal inpainting, respectively. Through the analysis of the results, we show that the proposed GAN-based inpainting method significantly improves the accuracy of AMC.