Automatic modulation classification method based on transfer learning

Deep learning has achieved considerable progress in the field of modulation classification; however, it is often limited by the consistency of the distribution between training and testing data. A novel multi-scale attention transfer learning architecture (MATLA) has been designed to assist cross-domain identification in order to overcome the problem of modulation classification with inconsistent data distribution caused by variable sample rates of source and target domains. This framework employs three parallel convolutional kernels with varying scales to extract features at different granularities. Moreover, the architecture integrates a multi-scale attention mechanism, which bolsters the extraction of discriminative features by emphasizing the weights of salient features. To effectively align features from the source and target domains, multiple kernel maximum mean discrepancy (MK-MMD) is utilized to measure the divergence of feature distributions in the reproducing kernel Hilbert space (RKHS) between the two domains. Additionally, to mitigate internal variation within the source domain features and enhance their consistency and stability, multiple kernel center loss (MKCL) is proposed. According to experimental data, the suggested approach outperforms a number of different network models and domain adaption techniques, achieving a recognition accuracy of 83.42% when the signal-to-noise ratio surpasses 0 dB.