TY - JOUR
T1 - Machine Learning-Enabled Cooperative Spectrum Sensing for Non-Orthogonal Multiple Access
AU - Shi, Zhenjiang
AU - Gao, Wei
AU - Zhang, Shangwei
AU - Liu, Jiajia
AU - Kato, Nei
N1 - Funding Information:
Manuscript received November 1, 2019; revised March 29, 2020; accepted May 10, 2020. Date of publication May 27, 2020; date of current version September 10, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61771374, Grant 61771373, Grant 61801360, and Grant 61601357, in part by the Natural Science Basic Research Program of Shaanxi under Grant 2020JC-15 and Grant 2020JM-109, in part by the Fundamental Research Funds for the Central Universities under Grant 3102019PY005, Grant 31020190QD040, and Grant 31020200QD010, in part by the Special Funds for Central Universities Construction of World-Class Universities (Disciplines) and Special Development Guidance under Grant 06390-20GH020114, and in part by the China 111 Project under Grant B16037. The associate editor coordinating the review of this article and approving it for publication was X. Chen. (Corresponding author: Jiajia Liu.) Zhenjiang Shi is with the State Key Laboratory of Integrated Services Networks, School of Cyber Engineering, Xidian University, Xi’an 710071, China (e-mail: shizhenjiang_xd@163.com).
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - Cooperative spectrum sensing
KW - machine learning
KW - non-orthogonal multiple access
UR - http://www.scopus.com/inward/record.url?scp=85091175697&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091175697&partnerID=8YFLogxK
U2 - 10.1109/TWC.2020.2995594
DO - 10.1109/TWC.2020.2995594
M3 - Article
AN - SCOPUS:85091175697
SN - 1536-1276
VL - 19
SP - 5692
EP - 5702
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 9
M1 - 9102451
ER -