Spectrum Sensing Using Multiple Large Eigenvalues and Its Performance Analysis

Authors

Ming Jin, Ningbo UniversityFollow
Qinghua Guo, University of WollongongFollow
Youming Li, Ningbo University
Jiangtao Xi, University of WollongongFollow
Defeng (David) Huang, University of Western AustraliaFollow

RIS ID

129213

Publication Details

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.

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.

Grant Number

ARC/DE120101266