On Robust Spectrum Sensing Using M-estimators of Covariance Matrix

TL;DR

This paper introduces a robust spectrum sensing method for cognitive radio networks using Tyler's M-estimator, which outperforms traditional eigenvalue-based detectors in impulsive noise environments without needing noise power knowledge.

Contribution

The paper proposes a novel eigenvalue-based spectrum sensing technique utilizing Tyler's M-estimator, enhancing robustness against impulsive noise without requiring noise distribution or power information.

Findings

Outperforms traditional eigenvalue-based detectors in impulsive noise.

Does not require noise power knowledge.

Shows superior detection performance in simulations.

Abstract

In this paper, we consider the spectrum sensing in cognitive radio networks when the impulsive noise appears. We propose a class of blind and robust detectors using M-estimators in eigenvalue based spectrum sensing method. The conventional eigenvalue based method uses statistics derived from the eigenvalues of sample covariance matrix(SCM) as testing statistics, which are inefficient and unstable in the impulsive noise environment. Instead of SCM, we can use M-estimators, which have good performance under both impulsive and non-impulsive noise. Among those M-estimators, We recommend the Tyler's M-estimator instead, which requires no knowledge of noise distribution and have the same probability of false alarm under different complex elliptically symmetric distributions. In addition, it performs better than the detector using sample covariance matrix when the noise is highly impulsive. It should be emphasized that this detector does not require knowledge of noise power which is required by the energy detection based methods. Simulations show that it performs better than conventional detector using sample covariance matrix in a highly impulsive noise environment.