Voting-Based Deep Convolutional Neural Networks (VB-DCNNs) for M-QAM and M-PSK Signals Classification

• Published in: Electronics (Basel) Vol. 12; no. 8; p. 1913
• Main Authors: Talha, Muhammad, Sarfraz, Mubashar, Rahman, Atta, Ghauri, Sajjad A., Mohammad, Rami M., Krishnasamy, Gomathi, Alkharraa, Mariam
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2023
• Subjects: Accuracy; Algorithms; Artificial neural networks; Classification; Codes; Communication; Datasets; Decision trees; Deep learning; Fading channels; Methods; Mobile communication systems; Model accuracy; Modulation; Modulation (Electronics); Neural networks; Pattern recognition; Phase shift keying; Random noise; Receivers & amplifiers; Signal classification; Signal processing; Software; Time series; Training; Waveforms; Wireless communication systems; Pakistan
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics12081913

Automatic modulation classification (AMC) using convolutional neural networks (CNNs) is an active area of research that has the potential to improve the efficiency and reliability of wireless communication systems significantly. AMC is the approach used in a communication system to detect the type of modulation format at the receiver end. This paper proposes a voting-based deep convolutional neural network (VB-DCNN) for classifying M-QAM and M-PSK signals. M-QAM and M-PSK signal waveforms are generated and passed through the fading channel in the presence of additive white Gaussian noise (AWGN). The VB-DCNN extracts features from the input signal through convolutional layers, and classification is performed on these features. Multiple network instances are trained on different subsets of training data in the VB-DCNN. A network instance predicts the input signal during testing. Based on the votes, the final prediction is made. Different simulation experiments are carried out to analyze the performance of the trained network, and the DCNN is designed with the Deep Neural Network Toolbox in MATLAB. The generated frames are divided into training, validation, and test datasets. Lastly, the classification accuracy of the trained network is determined using test frames. The proposed model’s accuracy is near to 100% at lower SNRs. The simulation results show the superiority of the proposed VB-DCNN compared to existing state-of-the-art techniques.