Original paper

Analysis of Spectrum Occupancy Using Machine Learning Algorithms

Volume: 65, Issue: 9, Pages: 6853 - 6860
Published: Sep 1, 2016
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...
Paper Details
Title
Analysis of Spectrum Occupancy Using Machine Learning Algorithms
Published Date
Sep 1, 2016
Volume
65
Issue
9
Pages
6853 - 6860
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