Key result
K-nearest neighbor achieves ~100% accuracy and fastest speeds for primary user detection among ML techniques.
Why the study?
Spectrum management lacks information on primary user presence, accurate user detection in low SNR values, and faces high computational costs.
KNN algorithm provides the best performance and minimized time for primary user detection in cognitive radio networks at low SNR.
KNN may enhance spectrum sensing efficiency at low SNR; hypothesis-generating and requires prospective validation before network adoption.
Spectrum decision is an important and crucial task for the secondary user to avail the unlicensed spectrum for transmission. Managing the spectrum is an efficient one for spectrum sensing. Determining the primary user presence in the spectrum is an essential work for using the licensed spectrum of primary user. The information which lacks in managing the spectrum are the information about the primary user presence, accuracy in determining the existence of user in the spectrum, the cost for computation and difficult in finding the user in low signal-to noise ratio (SNR) values. The proposed system overcomes the above limitations. In the proposed system, the various techniques of machine learning like decision tree, support vector machines, naive bayes, ensemble based trees, nearest neighbour’s and logistic regression are used for testing the algorithm. As a first step, the spectrum sensing is done in two stages with Orthogonal Frequency Division Multiplexing and Energy Detection algorithm at the various values of SNR. The results generated from the above algorithm is used for database generation. Next, the different machine learning techniques are trained and compared for the results produced by different algorithms with the characteristics like speed, time taken for training and accuracy in prediction. The accuracy and finding the presence of the user in the spectrum at low SNR values are achieved by all the algorithms. The computation cost of the algorithm differs from each other. Among the tested techniques, k-nearest neighbour (KNN) algorithm produces the better performance in a minimized time.
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Pethunachiyar et al. (2021) studied Spectrum sensing in cognitive radio networks (n=5,000). K-nearest neighbor (KNN) algorithm vs. Other machine learning algorithms (Decision Trees, SVM, Naive Bayes, Logistic Regression) was evaluated on Accuracy of primary user detection. The K-nearest neighbor algorithm achieved 100% accuracy in primary user detection with the fastest prediction speed and training time among the tested machine learning techniques.
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