Sensitive White Space Detection with Spectral Covariance Sensing

TL;DR

This paper introduces a spectral covariance sensing algorithm for spectrum detection in cognitive radio, significantly improving sensitivity and robustness to noise uncertainty compared to existing methods.

Contribution

The paper presents a novel spectral covariance sensing algorithm that exploits frequency domain correlations, offering enhanced sensitivity and noise robustness for spectrum sensing.

Findings

SCS improves sensitivity by 3 dB over state-of-the-art methods.

SCS is highly robust to noise uncertainty.

Verified with real digital TV signals in simulations.

Abstract

This paper proposes a novel, highly effective spectrum sensing algorithm for cognitive radio and whitespace applications. The proposed spectral covariance sensing (SCS) algorithm exploits the different statistical correlations of the received signal and noise in the frequency domain. Test statistics are computed from the covariance matrix of a partial spectrogram and compared with a decision threshold to determine whether a primary signal or arbitrary type is present or not. This detector is analyzed theoretically and verified through realistic open-source simulations using actual digital television signals captured in the US. Compared to the state of the art in the literature, SCS improves sensitivity by 3 dB for the same dwell time, which is a very significant improvement for this application. Further, it is shown that SCS is highly robust to noise uncertainty, whereas many other spectrum sensors are not.