LAGNet: A Hybrid Deep Learning Model for Automatic Modulation Recognition
Zhuo Li, Guangyue Lu, Yuxin Li, Hao Zhou, Huan Li · 2024
Automatic Modulation Recognition (AMR) is becoming increasingly crucial in the industrial internet, and it ensures more reliable communication for devices within this vast and intricate network. Although recent application of neural networks, notably Graph Convolutional Network (GCN) in the AMR domain shows promising results, it fails to account for temporal features within modulation signals, resulting in a loss of recognition precision. Given that modulation signals possess both time and space features, this paper proposes a hybrid deep learning model named LAGNet, which combines Long Short-Term Memory (LSTM) and GCN. The model uses an attention mechanism to map the LSTM outputs into a graph and then employs the GCN to extract the spatial features of the signal. Subsequently, the temporal features and the spatial features are combined together for modulation signal classification. Experimental results show that LAGNet not only outperforms several advanced models in classification accuracy across all signal-to-noise ratios, but also requires fewer learnable parameters.