Spectrum Sensing Algorithms for Cognitive Radio Based on Statistical Covariances

TL;DR

This paper introduces spectrum sensing algorithms for cognitive radio that utilize statistical covariances of received signals, enabling detection without prior knowledge of signal, noise, or channel parameters, verified through simulations.

Contribution

The paper proposes novel covariance-based spectrum sensing algorithms that do not require prior signal or noise information and are validated through theoretical analysis and simulations.

Findings

High detection probability demonstrated in simulations

Algorithms do not require signal, channel, or noise power knowledge

Effective for narrowband, DTV, and multi-antenna signals

Abstract

Spectrum sensing, i.e., detecting the presence of primary users in a licensed spectrum, is a fundamental problem in cognitive radio. Since the statistical covariances of received signal and noise are usually different, they can be used to differentiate the case where the primary user's signal is present from the case where there is only noise. In this paper, spectrum sensing algorithms are proposed based on the sample covariance matrix calculated from a limited number of received signal samples. Two test statistics are then extracted from the sample covariance matrix. A decision on the signal presence is made by comparing the two test statistics. Theoretical analysis for the proposed algorithms is given. Detection probability and associated threshold are found based on statistical theory. The methods do not need any information of the signal, the channel and noise power a priori. Also, no synchronization is needed. Simulations based on narrowband signals, captured digital television (DTV) signals and multiple antenna signals are presented to verify the methods.