Machine Learning Techniques in Spectrum Sensing

Authors

  • R. K. Kavya  PG Scholar, Department of ECE, Jerusalem College of Engineering, Pallikaranai, Tamil Nadu, Chennai, India
  • Ms. T. Tamilselvi  Associate Professor, Department of ECE, Jerusalem College of Engineering, Pallikaranai, Tamil Nadu, Chennai, India

Keywords:

Gaussian Mixture Model, cooperative spectrum sensing, cognitive radio, Support Vector Machine.

Abstract

The spectrum shortage brought on by the development of new technologies can be minimized through the use of cognitive radio (CR) technology. Cognitive networks struggle with the hidden primary user problem because the secondary user may classify the spectrum occupancy incorrectly. As a result, machine learning-based cooperative spectrum sensing (CSS) was used to solve this challenge. Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) are two methods of machine learning approaches, where GMM is an unsupervised learning methodology and SVM is a supervised learning technique. The two phases of the aforementioned techniques—classification and training—determine the classes for channels that are available and unavailable. With a mixture of Gaussian density distributions, training features are determined using the Gaussian Mixture Model. A subset of training vectors for the SVM is used to create the decision surface. By increasing the space between the training and separation vectors, this is accomplished.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2023-04-30

Issue

Volume 10, Issue 2, March-April-2023

Section

Research Articles

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How to Cite

[1]
R. K. Kavya, Ms. T. Tamilselvi "Machine Learning Techniques in Spectrum Sensing" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 2, pp.739-746, March-April-2023.