DiDbKDT: A Cross-SNR Modulation Recognition Scheme With Dual-Input and Dual-Branch Based on Knowledge Distillation for IQ-AP Data

Modulation recognition accuracy is crucial in communication reconnaissance. In this study, we propose DiDbKDT, a dual-input dual-branch network architecture for cross-SNR modulation recognition. The model leverages knowledge distillation and integrates IQ-AP data as inputs. Considering the differences in data features under different SNR scenarios and combining the respective features of IQ and AP data, we divide the SNR interval into two segments and use the IQ and AP data as the inputs of the low-SNR and high-SNR networks respectively. To balance the computational complexity and recognition rate, in the DiDbKDT network model, we utilize the knowledge distillation technique. We deploy the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) in the low-SNR and high-SNR scenarios respectively as teacher models to quickly learn the internal modulation features of IQ and AP data. In the student model, we adopt the Transformer to further mine the global features of the data and the correlation relationships between different features, thus achieving more accurate modulation recognition and classification. We also introduce a data augmentation technique based on the channelization scenario to further improve the robustness of the model. Experimental results on six types of datasets, namely RML2016.10a/b/c, RML2018.01a, HisarMod, and MiGouMod, demonstrate that the proposed DiDbKDT outperforms the existing Automatic Modulation Recognition (AMR) methods and has higher parameter efficiency.