Interference Classification Using Deep Neural Networks
This work addresses interference classification for signal processing applications, but it is incremental as it applies existing deep learning techniques to a new problem.
The paper tackled interference classification in signals by proposing a deep neural network method using power-spectral density and cyclic spectrum features, achieving better accuracy with PSD and feed-forward networks compared to classic methods.
The recent success in implementing supervised learning to classify modulation types suggests that other problems akin to modulation classification would eventually benefit from that implementation. One of these problems is classifying the interference type added to a signal-of-interest, also known as interference classification. In this paper, we propose an interference classification method using a deep neural network. We generate five distinct types of interfering signals then use both the power-spectral density (PSD) and the cyclic spectrum of the received signal as input features to the network. The computer experiments reveal that using the received signal PSD outperforms using its cyclic spectrum in terms of accuracy. In addition, the same experiments show that the feed-forward networks yield better accuracy than classic methods. The proposed classifier aids the subsequent stage in the receiver chain with choosing the appropriate mitigation algorithm and also can coexist with modulation-classification methods to further improve the classifier accuracy.