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

Spectrum Sensing Based on STFT-ImpResNet for Cognitive Radio

“…The traditional spectrum sensing methods can be broadly categorized into energy detection (ED) [6,7], matched filter detection [8], cyclostationary feature detection [9], waveform-based sensing [10], and covariance-based detection [11], etc. However, the predefined threshold set by the traditional method has a dramatic influence on the detection probability.…”

CM-LSTM Based Spectrum Sensing

“…Many scholars form a sampled signal matrix and calculate its covariance matrix by acquiring the perceptual data of multiple SUs. The signal covariance matrix is used as the input data of the model [ 14 , 15 , 16 ]. Compared with using the energy features of the signal data, etc., this improves the performance of spectrum sensing.…”