Automatic Modulation Classification in Impulsive Noise: Hyperbolic-Tangent Cyclic Spectrum and Multibranch Attention Shuffle Network

Automatic modulation classification plays an essential role in cognitive communication systems. Traditional automatic modulation classification approaches are primarily developed under Gaussian noise assumptions. Nevertheless, recent empirical studies show that impulsive noise has emerged in numerous wireless communication systems. The bursty nature of impulsive noise fundamentally challenges the applicability of the traditional automatic modulation classification approaches. To accurately identify the modulation schemes in impulsive noise environment, in this article, we propose a novel modulation classification approach through using hyperbolic-tangent cyclic spectrum and multibranch attention shuffle neural networks. First, based on the designed hyperbolic-tangent autocorrelation function, hyperbolic-tangent cyclic spectrum is proposed to effectively suppress the impulsive noise and extract the discriminating features. Then, based on the hyperbolic-tangent cyclic spectrum, a novel deep shuffle neural network is proposed as a classifier to perform the modulation classification through the multibranch attention mechanism to reweight all the features. Both the numerical and real-data experimental results demonstrate that the proposed algorithm can correctly classify modulation schemes with high accuracy and robustness in impulsive noise.