Abstract
In cognitive radio network some of the important functionalities is spectrum sensing. It plays a very vital role for unlicensed system to operate efficiently and to provide the required improvement in spectrum efficiency. If the spectrum, which is sensed is in idle state allow the unauthorized users (secondary users) to use the spectrum. Machine learning algorithms are used for spectrum sensing in cognitive radio networks. They are weighted K-nearest neighbor, Support Vector Machine (SVM) which comes under supervised learning and Gaussian Mixture Model (GMM), K-means clustering which comes under unsupervised learning-based classification techniques. In this paper rigorous survey is done by using machine learning algorithms to review various methodologies used in spectrum sensing like K-nearest -neighbor, GMM, K-means clustering and SVM.
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Kulkarni, V.S., Dhope(Shendkar), T.S., Karve, S., Chippalkatti, P., Jadhav, A. (2021). Machine Learning Approach in Cooperative Spectrum Sensing for Cognitive Radio Network: Survey. In: Pawar, P.M., Balasubramaniam, R., Ronge, B.P., Salunkhe, S.B., Vibhute, A.S., Melinamath, B. (eds) Techno-Societal 2020. Springer, Cham. https://doi.org/10.1007/978-3-030-69921-5_7
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DOI: https://doi.org/10.1007/978-3-030-69921-5_7
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