Small sample signal modulation recognition method based on transfer learning

In view of the problem that traditional deep learning requires a large amount of labeled signal data set but the measured data is difficult to meet, this paper proposes a small sample recognition method based on transfer learning (TLSSM), which transfers the recognition model from large-scale simulation data to the small sample measured data recognition model. It can also achieve better recognition performance in the case of a small number of samples. Simulation experiments show that when TLSSM has 100 samples of each type of signal in the training set, the signal recognition accuracy of 5-10dB mixed signal is as high as 90%, which is 84% smaller than the data required by traditional convolutional network. It proves that the method proposed in this paper is effective and feasible in reducing the size of the training set and improving the speed of recognition.