Robust Automatic Modulation Classification Using Asymmetric Trilinear Attention Net with Noisy Activation Function

Nowadays, automatic modulation classification (AMC) plays an essential role in the cognitive radio based non-cooperative wireless communication system. Although numerous deep learning models have been developed for AMC, it remains challenging to effectively recognize various modulation schemes in complicated signal-to-noise ratio (SNR) conditions. In this paper, we introduce a robust AMC method based on the asymmetric trilinear attention net (Tri-Net) with noisy activation function. In Tri-Net, the asymmetric trilinear representation module is developed to deal with various channels of received radio signals and extract rich features to improve the generalization ability. Then a hybrid coding module with squeeze and excitation (SE) blocks-based attention mechanism is designed to help the model adapt to fluctuating SNRs. Finally, the predicted modulation schemes can be output through the classification module. During the training process, the noisy rectified linear unit (ReLU) is proposed guiding the model to explore the convergence position closer to the global optimum. Extensive experiments on practical and simulation communication applications demonstrate that Tri-Net achieves superior classification performance compared with a series of state-of-the-art deep learning models, especially at low SNRs.