Analysis of Spectrum Occupancy Using Machine Learning Algorithms

In this paper, the authors analyze the spectrum occupancy in cognitive radio networks (CRNs) using different machine learning techniques. Both supervised techniques [naive Bayesian classifier (NBC), decision trees (DT), support vector machine (SVM), linear regression (LR)] and unsupervised algorithms [hidden Markov model (HMM)] are studied to find the best technique with the highest classification accuracy (CA). A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and the CA is performed. The classified occupancy status is further utilized to evaluate the blocking probability of secondary user for future time slots, which can be used by system designers to define spectrum-allocation and spectrum-sharing policies. Numerical results show that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, the authors proposed a new SVM algorithm by combining it with a firefly algorithm (FFA), which is shown to outperform all the other algorithms.

• Record URL:
  • https://doi.org/10.1109/TVT.2015.2487047
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2016, IEEE.
• Authors:
  • Azmat, Freeha
  • Chen, Yunfei
  • Stocks, Nigel
• Publication Date: 2016-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 6853-6860
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 65
  • Issue Number: 9
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Classification; Electromagnetic spectrum; Machine learning; Wireless communication systems
• Uncontrolled Terms: Cognitive radio networks; Support vector machines
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01614161
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 25 2016 9:50AM