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

Zhenjiang Shi, Wei Gao, Shangwei Zhang, Jiajia Liu, Nei Kato

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)


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.

Original languageEnglish
Article number9102451
Pages (from-to)5692-5702
Number of pages11
JournalIEEE Transactions on Wireless Communications
Issue number9
Publication statusPublished - 2020 Sept


  • Cooperative spectrum sensing
  • machine learning
  • non-orthogonal multiple access


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