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Spectrum Sensing Using Multiple Large Eigenvalues and Its Performance Analysis

Journal Article


Abstract


  • Cognitive radio (CR) is a promising technology to address the challenge of spectrum scarcity due to the massive number of objects in the Internet of Things (IoT). Equipping IoT objects with CR capability can also alleviate interference situations and achieve seamless connectivity in IoT. This work deals with CR spectrum sensing and proposes a new eigenvalue-based detector by exploiting the summation of multiple large eigenvalues of the covariance matrix of received signals. By analyzing the distribution of the sum of the dependent large eigenvalues, we derive an approximate but explicit expression for the theoretical performance of the proposed detector. The theoretical analysis of the proposed detector is validated and its superior performance is demonstrated with real world signals. It is shown that the proposed detector outperforms the existing eigenvalue-based detectors and is more robust against noise uncertainty.

Authors


  •   Jin, Ming (external author)
  •   Guo, Qinghua
  •   Li, Youming (external author)
  •   Xi, Jiangtao
  •   Huang, Defeng (David) (external author)

Publication Date


  • 2019

Citation


  • M. Jin, Q. Guo, Y. Li, J. Xi & D. Huang, "Spectrum Sensing Using Multiple Large Eigenvalues and Its Performance Analysis," IEEE Internet of Things Journal, vol. 6, (1) pp. 776-789, 2019.

Scopus Eid


  • 2-s2.0-85050197156

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2409

Number Of Pages


  • 13

Start Page


  • 776

End Page


  • 789

Volume


  • 6

Issue


  • 1

Place Of Publication


  • United States

Abstract


  • Cognitive radio (CR) is a promising technology to address the challenge of spectrum scarcity due to the massive number of objects in the Internet of Things (IoT). Equipping IoT objects with CR capability can also alleviate interference situations and achieve seamless connectivity in IoT. This work deals with CR spectrum sensing and proposes a new eigenvalue-based detector by exploiting the summation of multiple large eigenvalues of the covariance matrix of received signals. By analyzing the distribution of the sum of the dependent large eigenvalues, we derive an approximate but explicit expression for the theoretical performance of the proposed detector. The theoretical analysis of the proposed detector is validated and its superior performance is demonstrated with real world signals. It is shown that the proposed detector outperforms the existing eigenvalue-based detectors and is more robust against noise uncertainty.

Authors


  •   Jin, Ming (external author)
  •   Guo, Qinghua
  •   Li, Youming (external author)
  •   Xi, Jiangtao
  •   Huang, Defeng (David) (external author)

Publication Date


  • 2019

Citation


  • M. Jin, Q. Guo, Y. Li, J. Xi & D. Huang, "Spectrum Sensing Using Multiple Large Eigenvalues and Its Performance Analysis," IEEE Internet of Things Journal, vol. 6, (1) pp. 776-789, 2019.

Scopus Eid


  • 2-s2.0-85050197156

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2409

Number Of Pages


  • 13

Start Page


  • 776

End Page


  • 789

Volume


  • 6

Issue


  • 1

Place Of Publication


  • United States