Multi-Frame Dual-Stream 2DCNN-LSTM Model for Automatic Modulation Recognition

This paper proposes a multi-frame dual-stream 2DCNN-LSTM model (MF-DS-2DCNN-LSTM) for automatic modulation recognition. The model discretizes long sequences into two-dimensional frame structures and uses 2D CNN and LSTM together to model the spatiotemporal features of multi-channel IQ/AP signals. By employing a frame-based strategy, the original signal is reshaped into small images, with the 2D CNN extracting intra-frame spatial structures and inter-channel interaction features, while the LSTM captures the temporal evolution between frames. This approach integrates hierarchical modeling concepts from image processing and video analysis, and utilizes the Crested Porcupine Optimizer for hyperparameter tuning. Simulations show that, when recognizing nine modulation types, the model significantly outperforms methods such as CLDNN, achieving an average accuracy of 91.4% under high-SNR conditions (SNR above 2 dB). Moreover, the model maintains an accuracy of over 90% in small-sample training scenarios for SNRs above 4 dB. After optimization with the Crested Porcupine Optimizer, the models performance improved by 2.2%, and a 20.7% reduction in parameters was achieved.