Modulation Pattern Identification Algorithm for Model Transfer Based on Machine Learning

To overcome the shortcomings of traditional modulation identification methods in small sample signal processing, this paper proposes a new model transfer modulation identification algorithm that integrates machine learning. The algorithm first uses a combination of convolutional neural network (CNN) and long short-term memory network (LSTM) to fully tap and utilize its feature extraction capabilities in the spatial and temporal dimensions. It significantly improves the performance of the model in these two dimensions through a parallel structure, which can ensure high efficiency and accuracy in signal recognition tasks. Then, through model migration technology, the common features in the pre-trained model are retained and fine-tuned to adapt to new signal recognition needs, effectively solving the problem of small sample signal recognition. Experimental results show that the algorithm proposed in this paper significantly improves signal recognition accuracy, with an average accuracy rate of 96%. In the 16APSK and 16QAM signal recognition tasks, the accuracy is as high as 100% and 99%, respectively, which demonstrates excellent performance.