Deep and Ensemble Learning to Win the Army RCO AI Signal Classification Challenge
Andres Vila
Donna Branchevsky
Kyle Logue
Sebastian Olsen
Esteban Valles
Darren Semmen
Alex Utter
Eugene Grayver
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.
modulation classification, neural networks, deep learning, machine learning, ensemble learning, wireless communications, signals intelligence, probability calibration
DOI10.25080/Majora-7ddc1dd1-003