A Fast Automatic Modulation Classification Based on STFT Using Hybrid Deep Neural Network

Automatic modulation classification is used in various applications, including satellite communication systems, military communication, and submarine communications. In this paper, the automatic classification of modulation types is done using a two-stage method that combines a short-time Fourier transform (STFT) and a hybrid deep neural network (HDNN). At the first stage, using the STFT as a data source, the time-frequency information is retrieved from the modulated signals. A hybrid deep neural network feed two-dimensional (2D) images as inputs. In the second stage, the HDNN feeds the 2D time-frequency data to classify the various modulation types. Six various types of modulation schemes, including amplitude-shift keying, frequency-shift keying, phase-shift keying, quadrature amplitude-shift keying, quadrature frequency-shift keying, and quadrature phase-shift keying, are recognized automatically in the SNR range of 0 to 25 dB. An exhaustive computer simulation has been performed to evaluate the performance of the proposed digital modulation classification method. The simulation results show that, in comparison with the existing methods, our proposed method performs well and significantly reduces the processing time.