Large-Scale Classification of Shortwave Communication Signals with Machine Learning
This work addresses the need for automated, blind classification of shortwave signals for applications in communication monitoring or security, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackles the problem of classifying 160 types of shortwave radio signals, which are challenging due to diverse modulations and ionospheric effects, and achieves up to 90% accuracy with only 1 second of observation time using a deep convolutional neural network.
This paper presents a deep learning approach to the classification of 160 shortwave radio signals. It addresses the typical challenges of the shortwave spectrum, which are the large number of different signal types, the presence of various analog modulations and ionospheric propagation. As a classifier a deep convolutional neural network is used, that is trained to recognize 160 typical shortwave signal classes. The approach is blind and therefore does not require preknowledge or special preprocessing of the signal and no manual design of discriminative features for each signal class. The network is trained on a large number of synthetically generated signals and high quality recordings. Finally, the network is evaluated on real-world radio signals obtained from globally deployed receiver hardware and achieves up to 90% accuracy for an observation time of only 1 second.