Cognitive Radio Oriented Wireless Networks. 12th International Conference, CROWNCOM 2017, Lisbon, Portugal, September 20-21, 2017, Proceedings

Research Article

Spectrum Occupancy Classification Using SVM-Radial Basis Function

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  • @INPROCEEDINGS{10.1007/978-3-319-76207-4_10,
        author={Mitul Panchal and D. Patel and Sanjay Chaudhary},
        title={Spectrum Occupancy Classification Using SVM-Radial Basis Function},
        proceedings={Cognitive Radio Oriented Wireless Networks. 12th International Conference, CROWNCOM 2017, Lisbon, Portugal, September 20-21, 2017, Proceedings},
        proceedings_a={CROWNCOM},
        year={2018},
        month={3},
        keywords={Big data Spectrum occupancy Spectrum measurement Communication Machine learning Classification},
        doi={10.1007/978-3-319-76207-4_10}
    }
    
  • Mitul Panchal
    D. Patel
    Sanjay Chaudhary
    Year: 2018
    Spectrum Occupancy Classification Using SVM-Radial Basis Function
    CROWNCOM
    Springer
    DOI: 10.1007/978-3-319-76207-4_10
Mitul Panchal1,*, D. Patel1,*, Sanjay Chaudhary1,*
  • 1: Ahmedabad University
*Contact email: mitul.panchal@iet.ahduni.edu.in, dhaval.patel@ahduni.edu.in, sanjay.chaudhary@ahduni.edu.in

Abstract

With recent development in wireless communication, efficient spectrum utilization is major area of concern. Spectrum measurement studies conducted by wireless communication researchers reveals that the utilization of spectrum is relatively low. In this context, we analyzed big spectrum data for actual spectrum occupancy in spectrum band using different machine learning techniques. Both supervised [Naive Bayes classifier (NBC), K-NN, Decision Tree (DT), Support Vector Machine with Radial Basis Function (SVM-RBF)] and unsupervised algorithms [Neural Network] are applied to find the best classification algorithm for spectrum data. Obtained results shows that combination of SVM-RBF is the best classifier for spectrum database with highest classification accuracy appropriately for distinguishing the class vector in the busy and idle state. We made analysis-based on empirical SVM-RBF model to identify actual duty cycle on the particular band across four mid-size location at Ahmedabad Gujarat.