Deep and Ensemble Learning to Win the Army RCO AI Signal Classification Challenge


Automatic modulation classification is a challenging problem with multiple applications including cognitive radio and signals intelligence. Most of the existing efforts to solve this problem are only applicable when the signal to noise ratio (SNR) is high and/or long observations of the signal are available. Recent work has focused on applying shallow and deep machine learning (ML) to this problem. In this paper, we present an exploration of such deep learning and ensemble learning techniques that was used to win the Army Rapid Capability Office (RCO) 2018 Signal Classification Challenge. An expert feature extraction and shallow learning approach is discussed in a simultaneous publication. We evaluated multiple state-of-the-art deep learning network architectures and adapted them to work in the RF signal domain instead of the image/computer-vision domain. The best deep learning methods were merged with the best expert feature extraction and shallow learning methods using ensemble learning. Finally, the ensemble classifier was calibrated to obtain marginal gains. The methods discussed are capable of correctly classifying waveforms at -10 dB SNR with over 63% accuracy and signals at +10 dB SNR with over 95% accuracy from an Army RCO provided training set.

Keywords:modulation classificationneural networksdeep learningmachine learningensemble learningwireless communicationssignals intelligenceprobability calibration