Cyclostationary Spectrum Sensing in Cognitive Radios Using FRESH Filters

TL;DR

This paper introduces a novel spectrum sensing method for cognitive radios using FRESH filters to detect primary users at low SNR, outperforming traditional energy and cyclostationary detection techniques.

Contribution

It proposes the use of FRESH filters for low SNR spectrum sensing and demonstrates their effectiveness through simulation, establishing their superiority over existing methods.

Findings

FRESH filters enable effective cyclostationary signal estimation at low SNR.

The proposed method outperforms energy detection and traditional cyclostationary detection.

Simulation confirms the convergence and improved detection performance.

Abstract

This paper deals with spectrum sensing in Cognitive Radios to enable unlicensed secondary users to opportunistically access a licensed band. The ability to detect the presence of a primary user at a low signal to noise ratio (SNR) is a challenging prerequisite to spectrum sensing and earlier proposed techniques like energy detection and cyclostationary detection have only been partially successful. This paper proposes the use of FRESH (FREquency SHift) filters [1] to enable spectrum sensing at low SNR by optimally estimating a cyclostationary signal using its spectral coherence properties. We establish the mean square error convergence of the adaptive FRESH filter through simulation. Subsequently, we formulate a cyclostationarity based binary hypothesis test on the filtered signal and observe the resultant detection performance. Simulation results show that the proposed approach performs better than energy detection and cyclostationary detection techniques for spectrum sensing.