Classification Of Digital Modulated Signals Research Articles

Automatic modulation classification of digital modulation signals with stacked autoencoders

Modulation identification of the transmitted signals remain a challenging area in modern intelligent communication systems like cognitive radios. The computation of the distinct features from input data set and applying machine learning algorithms has been a well-known method in the classification of such signals. However, recently, deep neural networks, a branch of machine learning, have gained significant attention in the pattern recognition of complex data due to its superior performance. Here, we test the application of deep neural networks to the automatic modulation classification in AWGN and flat-fading channel. Three training inputs were used; mainly 1) In-phase and quadrature (I-Q) constellation points, 2) the centroids of constellation points employing the fuzzy C-means algorithm to I-Q diagrams, and 3) the high-order cumulants of received samples. The unsupervised learning from these data sets was done using the sparse autoencoders and a supervised softmax classifier was employed for the classification. The designing parameters for training single and 2-layer sparse autoencoders are proposed and their performance compared with each other. The results show that a very good classification rate is achieved at a low SNR of 0 dB. This shows the potential of the deep learning model for the application of modulation classification.

A novel modulation classification approach using Gabor filter network.

A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel.

USING OF THE BAYES DECISION RULES CLASSIFIER FOR DIGITAL MODULATION RECOGNITION SYSTEM

In this study, a pattern recognition system was used for the classification of digital modulated signals. In this pattern recognition system, the wavelet transform - entropy methods and Bayes classifier were used for feature extraction and classification. The Succession of Bayes classifier shows activity of used feature extraction method for the classification of digital modulated signals.