Spectrum Sensing in Low SNR Regime via Stochastic Resonance

TL;DR

This paper introduces a novel spectrum sensing method using stochastic resonance to improve detection in low SNR environments, enhancing reliability and speed in cognitive radio systems.

Contribution

It presents the first application of stochastic resonance for spectrum sensing, demonstrating improved detection performance in low SNR conditions.

Findings

Detection probability significantly increased

Average detection delay reduced

Effective in low SNR regimes

Abstract

Spectrum sensing is essential in cognitive radio to enable dynamic spectrum access. In many scenarios, primary user signal must be detected reliably in low signal-to-noise ratio (SNR) regime under required sensing time. We propose to use stochastic resonance, a nonlinear filter having certain resonance frequency, to detect primary users when the SNR is very low. Both block and sequential detection schemes are studied. Simulation results show that, under the required false alarm rate, both detection probability and average detection delay can be substantially improved. A few implementation issues are also discussed.