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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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
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Summary: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 model) as well as supervised learning algorithms (directed acyclic graph-support vector machine, K-nearest-neighbor and back-propagation neural network). In these solutions, multiple secondary users (SUs) collaborate to perceive the presence of primary users (PUs), and the state of each PU need to be detected precisely. Furthermore, the sensing accuracy is analyzed in detail from the aspects of the number of SUs, the training data volume, the average signal-to-noise ratio of receivers, the ratio of PUs' power coefficients, as well as the training time and test time. Numerical results illustrate the effectiveness of our proposed solutions.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2020.2995594