Machine learning model to classify modulation techniques using robust convolution neural network

Nadakuditi Durga Indira, Matcha Venu Gopala Rao


In wireless ccommunications receiver plays a main role to recognize modulation techniques which were used at the transmitter. While transferring information from transmitter to receiver, the receiver must retrieve original information. In order to achieve this goal we introduced a neural network architecture that recognizes the types of modulation techniques. The applications of deep learning can be categorized into classification and detection. The CNN architecture is used to perform feature extraction based on the layers to build a model which classifies the input data. A model that classifies the radio communication signals using deep learning method. The robust c (RCNN) is used to train the modulated signals; the transformations are used to help the neural network which estimate the signal to noise ratio of each signal ranges from -20dB to 18dB with loss and accuracy of 89.57% at SNR 0dB.


Deep learning; Machine learning; Neural network architecture; Batch normalization; Robust CNN;

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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