Automatic Modulation Classification Based on Wavelet Analysis and Convolution Neural Network

Abstract

Automatic modulation classification (AMC) of received unknown signals is critical in modern communication systems, enabling intelligent signal interception and spectrum management. In this paper, we propose a wavelet-based spectrum convolutional neural network (WS-CNN) model that integrates signal processing techniques with deep learning to achieve robust classification under challenging conditions, including noise, fading, and Doppler effects. The WS-CNN model is based on wavelet analysis and a convolutional neural network (CNN). Specifically, the proposed wavelet analysis, including wavelet threshold denoising, median filtering, and continuous wavelet transformation, is used for signal preprocessing to extract features and generate a compact 2D diagram. The 2D diagram is subsequently fed into the CNN for classification. The simulation results show that the proposed WS-CNN model achieves higher classification rates across a wide range of signal-to-noise ratios (SNRs) compared with existing methods.