A Noise-Robust Modulation Signal Classification Method Based on Continuous Wavelet Transform
Cenxin Peng, Wei Cheng, Zihao Song, Ruijie Dong · 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) · 2020
The feature extraction of Automatic Modulation Classification (AMC) is difficult, and the classification performance is poor, particularly for low SNRs and fading channels. To solve these problems, a combinatorial model based on Convolutional Neural Network (CNN) and Long-Short-Term Memory (LSTM) is proposed in this paper. This method transforms the time-domain signals to the two-dimensional time-frequency domain samples by Continuous Wavelet Transform (CWT). It then extracts in-depth features through the CNN-LSTM model to conduct classification on modulation signals. Analyses show that the proposed model yields an classification accuracy higher than 90% at varying SNR conditions ranging from -6dB to 12dB. Compared with traditional algorithms, this method effectively improves the problem of poor classification performance for low SNRs and possesses better robustness.