Abstract:

The technique of signal modulation identification based on deep learning has made significant progress in recent years, but most of the solutions are based on supervised learning methods which require a large number of labeled samples. It is well-known that labeling on signal data samples is difficult and costly. Therefore, a semi-supervised learning method is proposed, which is pre-trained through self-supervised contrast learning method with a large number of unlabeled samples, and the modulation identification network based on the pre-trained feature extraction network can be trained to convergence by a small number of labeled samples. In this way, the dependence on labeled samples can be reduced significantly. Experiments on RadioML2018.01A show that the proposed algorithm used 1% of the labeled samples can almost achieve the same identification performance as the supervised learning algorithm with the use of all of the labeled samples. Meanwhile, when trained on only 0.1% of the labeled samples, the identification model on 24 modulation types still achieved the accuracy of above 93% under the condition that the signal-to-noise ratio is equal to and over 8 dB.

Key words: deep learning, modulation identification, self-supervised contrast learning, model pretraining