A covariance matrix-based spectrum sensing technology exploiting stochastic resonance and filters

Abstract Cognitive radio (CR) is designed to implement dynamical spectrum sharing and reduce the negative effect of scarcity caused by exponential increase in number wireless devices. CR requires that sensing should detect licenced signals quickly accurately enable coexistence between primary secondary users without interference. However, with a low signal-to-noise ratio (SNR) still challenge systems. This paper proposes novel covariance matrix-based method using stochastic resonance (SR) filters. SR implemented enforce detection signal multiple antennas SNR conditions. The filters are equipped receiver interference segment noise frequency. Then, two test statistics computed likelihood (LRT) or maximum eigenvalues detector (MED) constructed sample matrix processed signals. simulation results exhibit performance proposed algorithms under various channel conditions, namely, additive white Gaussian (AWGN) Rayleigh fading channels. energy (ED) also compared LRT MED. demonstrate filter implementation can achieve considerable improvement strong background.