A Random Matrix Approach to Wide Band Spectrum Sensing: Unknown Noise Variance Case

TL;DR

This paper introduces a random matrix-based spectrum sensing method that effectively estimates noise variance under various unknown conditions, outperforming traditional methods in detection probability with fewer samples.

Contribution

The paper proposes a novel random matrix approach for wide band spectrum sensing that handles unknown noise variance and different subband scenarios, improving detection performance.

Findings

Superior detection probability compared to existing methods

Requires fewer samples than cyclo-stationary algorithms

More samples than energy detection methods

Abstract

In this paper three different scenarios in wide band spectrum sensing have been studied. While the signal and noise statistics are supposed to be unspecified, random matrixes have been utilized in order to estimate the noise variance. These scenarios are: 1- Number of subbands is specified and there is enough information regarding being used or being unused for each of them. 2- Number of subbands is known but there is no information about usage distribution among them. 3- Number of subbands is unknown. Simulation results showed the superior performance of the proposed scheme. Regarding the number of samples, the proposed method requires less number of samples compared to the cyclo-stationary spectrum sensing algorithms and more samples compared to the energy detection based methods. But, regarding the detection probability, the proposed method is superior compared to both other spectrum sensing methods.