Communication Signal Modulation Mechanism Based on Artificial Feature Engineering Deep Neural Network Modulation Identifier

• Published in: Wireless communications and mobile computing Vol. 2021; no. 1
• Main Authors: Lu, Fei, Shi, Zhenjiang, Su, Rijian
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 2021; Hindawi Limited
• Subjects: Algorithms; Artificial neural networks; Clustering; Communication; Constellations; Eigenvalues; Engineering; Entropy (Information theory); Feature extraction; Frequency domain analysis; Hypotheses; Hypothesis testing; Mathematical analysis; Modulation; Neural networks; Noise; Pattern recognition; Radon transformation; Rotation; Spectrum allocation; Support vector machines; Wavelet transforms; Wireless networks
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2021/9988651

Based on the characteristics of time domain and frequency domain recognition theory, a recognition scheme is designed to complete the modulation identification of communication signals including 16 analog and digital modulations, involving 10 different eigenvalues in total. In the in-class recognition of FSK signal, feature extraction in frequency domain is carried out, and a statistical algorithm of spectral peak number is proposed. This paper presents a method to calculate the rotation degree of constellation image. By calculating the rotation degree and modifying the clustering radius, the recognition rate of QAM signal is improved significantly. Another commonly used method for calculating the rotation of constellations is based on Radon transform. Compared with the proposed algorithm, the proposed algorithm has lower computational complexity and higher accuracy under certain SNR conditions. In the modulation discriminator of the deep neural network, the spectral features and cumulative features are extracted as inputs, the modified linear elements are used as neuron activation functions, and the cross-entropy is used as loss functions. In the modulation recognitor of deep neural network, deep neural network and cyclic neural network are constructed for modulation recognition of communication signals. The neural network automatic modulation recognizer is implemented on CPU and GPU, which verifies the recognition accuracy of communication signal modulation recognizer based on neural network. The experimental results show that the communication signal modulation recognizer based on artificial neural network has good classification accuracy in both the training set and the test set.