A spatiotemporal multi-stream learning framework based on attention mechanism for automatic modulation recognition

Automatic modulation recognition (AMR) plays an essential role in wireless communication systems. Our paper proposes a novel multi-stream neural network (MSNN) to extract the features in parallel from the amplitude, phase, frequency, and raw data of the modulated signal. The framework integrates convolutional neural networks (CNN) and bidirectional gated recurrent units (Bi-GRU) to extract features more effectively from the spatial and temporal characteristics with the assistance of two different attention mechanisms, convolutional block attention module (CBAM) and multi-head self-attention (MHSA). Simulation experiments show that the performance of our proposed algorithm is better than that of other state-of-the-art (SOTA) recognition algorithms on four widely used DeepSig datasets. The recognition accuracy of our proposed model exceeds 99% (18 dB), 93% (16 dB), 95% (20 dB), and 97% (20 dB) respectively on the datasets RML2016.04C, RML2016.10A, RML2016.10B, and RML2018.01A.