Improved Eigenvalue-based Spectrum Sensing via Sensor Signal Overlapping

TL;DR

This paper introduces an improved eigenvalue-based spectrum sensing method using overlapping subgroups to enhance detection performance without prior knowledge of the primary signal.

Contribution

It proposes a novel test statistic based on overlapping combinatorial matrices, boosting the eigenvalue-based detector's effectiveness.

Findings

Enhanced detection performance demonstrated through simulations

Larger maximum eigenvalue and trace in covariance matrix with overlapping

Improved ROC curves compared to non-overlapping methods

Abstract

Eigenvalue-based detectors are considered as an important method of spectrum sensing since they do not require the information about the primary user (PU) signal. In this paper we propose a method to improve the performance of the eigenvalue-based detector. The proposed method introduces a new test statistic based on combinatorial matrix with components which are overlapping subgroups extracted from the array of received signals. As a result, its covariance matrix has a larger maximum eigenvalue and trace value than the one without overlapping. Simulation results show that our proposed method can further improve the detection performance of the optimal eigenvalue-based detector. The paper also shows the effect of different overlapping methods on the receiver operating characteristic curve.