Cognitive radio network as sensors: Low signal-to-noise ratio collaborative spectrum sensing

This paper propose a function of covariance matrix based spectrum sensing approach for cognitive radio systems. The statistical covariance of signal and noise are usually different, so a binary hypothesis test on covariance matrix is employed to determine the existence of primary user. Collaborative sensing scenario is introduced for the proposed algorithm, in which each sensor only needs limited sample data for calculation and sends mediate result to fusion center. A performance comparison among different rational functions is provided, which shows different functions in this algorithm may have similar or distinct performance. So it is important to choose an appropriate function. The proposed algorithm has a reliable performance in very low signal-to-noise ratio (SNR) condition, and outperforms the Estimator-Correlator (EC) approach.

I. Introduction

Wireless communication systems totally rely on the use of radio frequency spectrum resource, while the spectrum resource is becoming more and more scarce. However, the spectrum resource is not utilized efficiently, the current utilization of a licensed spectrum varies from 15% to 85% [1]. Hence, cognitive radio (CR) has been proposed as a technology for dynamic spectrum access (DSA), which sense the spectrum space to find temporal and spatial spectral holes and finally access to that opportunistic frequency band for commumcations.