S. Geetha

Work place: Department of Computer Applications, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India

E-mail: geethamuthuraj2007@gmail.com


Research Interests: Artificial Intelligence, IoT


Mrs. S. Geetha was graduate her BBA degree in Business Administration & MCA Degree from Madurai Kamaraj University, Sivakasi, and pursuing her Ph.D under Anna University. Currently working as an Assistant Professor(Sr. Grade) in the Department of Computer Applications, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India. Her research interest in Predictive Analytics, Artificial Intelligence, Air Quality Prediction, and IoT.

Author Articles
Deep Learning based Real Time Radio Signal Modulation Classification and Visualization

By S. Rajesh S. Geetha Babu Sudarson S Ramesh S

DOI: https://doi.org/10.5815/ijem.2023.05.04, Pub. Date: 8 Oct. 2023

Radio Modulation Classification is implemented by using the Deep Learning Techniques. The raw radio signals where as inputs and can automatically learn radio features and classification accuracy. The LSTM (Long short-term memory) based classifiers and CNN (Convolutional Neural Network) based classifiers were proposed in this paper. In the proposed work, two CNN based classifiers are implemented such as the LeNet classifier and the ResNet classifier. For visualizing the radio modulation, a class activation vector (w) is used. Finally in the proposed work, it is performed the classification by using the Deep learning models like CNN and LSTM based modulation classifiers. These deep learning models extract the important radio features that are used for classification. Here, the bench mark dataset RadioML2016.10a is used. This is an open dataset which contains the modulated signal I and Q values fewer than ten modulation categories. After evolution of proposed model with bench mark dataset, it is applied with real time data collected through the SDR Dongle receiver. From the obtained real time signal, the modulation categories have been classified and visualized the radio features extracted from the radio modulation classifiers.

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