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...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE Transactions on Wireless Communications Vol. 19; no. 9; pp. 5692 - 5702
Main Authors:	Shi, Zhenjiang, Gao, Wei, Zhang, Shangwei, Liu, Jiajia, Kato, Nei
Format:	Journal Article
Language:	English Japanese
Published:	New York IEEE 01.09.2020 Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!