Machine Learning-Enabled Cooperative Spectrum Sensing for Non-Orthogonal Multiple Access

In this paper, multiple machine learning-enabled solutions are adopted to tackle the challenges of complex sensing model in cooperative spectrum sensing for non-orthogonal multiple access transmission mechanism, including unsupervised learning algorithms (K-Means clustering and Gaussian mixture mode...

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Vydané v:	IEEE Transactions on Wireless Communications Ročník 19; číslo 9; s. 5692 - 5702
Hlavní autori:	Shi, Zhenjiang, Gao, Wei, Zhang, Shangwei, Liu, Jiajia, Kato, Nei
Médium:	Journal Article
Jazyk:	English Japanese
Vydavateľské údaje:	New York IEEE 01.09.2020 Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Back propagation networks Cascading style sheets Cluster analysis Clustering Cooperative spectrum sensing Machine learning Machine learning algorithms Neural networks NOMA non-orthogonal multiple access Nonorthogonal multiple access Probabilistic models Receivers Sensors Signal to noise ratio Support vector machines Training Vector quantization Wireless communication
ISSN:	1536-1276, 1558-2248
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