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Analysis of spectrum occupancy using machine learning algorithms
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Azmat, Freeha, Chen, Yunfei and Stocks, Nigel G. (2016) Analysis of spectrum occupancy using machine learning algorithms. IEEE Transactions on Vehicular Technology, 65 (9). 6853 -6860. doi:10.1109/TVT.2015.2487047 ISSN 0018-9545.
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Official URL: http://dx.doi.org/10.1109/TVT.2015.2487047
Abstract
In this paper, we 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 compu-
tational 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 sys-
tem 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, we proposed a new SVM algorithm by combining it with a
firefly algorithm (FFA), which is shown to outperform all the other
algorithms.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| Library of Congress Subject Headings (LCSH): | Cognitive radio networks, Radio frequency, Hidden Markov models, Support vector machines | ||||||
| Journal or Publication Title: | IEEE Transactions on Vehicular Technology | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 0018-9545 | ||||||
| Official Date: | September 2016 | ||||||
| Dates: |
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| Volume: | 65 | ||||||
| Number: | 9 | ||||||
| Page Range: | 6853 -6860 | ||||||
| DOI: | 10.1109/TVT.2015.2487047 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Date of first compliant deposit: | 10 February 2016 | ||||||
| Date of first compliant Open Access: | 10 February 2016 |
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