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

Jin Lu,Jingjing Yang,Ming Huang

doi:10.1186/s13634-020-00710-6

Jin Lu, Jingjing Yang + Show 1 more

Open Access

https://doi.org/10.1186/s13634-020-00710-6

Copy DOI

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.

Highlights

Wireless mobile network services have grown rapidly and exhibited huge potentiality in the last few decades
5 Discussion From the simulation results above, it can be found that stochastic resonance (SR) can enhance the output signalto-noise ratio (SNR) under the premise of the adiabatic theory, which is ensured by the normalized scale transformation (NST) technology
The NST technology is introduced in SR to normalized the high frequency application to a low frequency expression

Summary

Introduction

Wireless mobile network services have grown rapidly and exhibited huge potentiality in the last few decades. The covariance-based detector (CBD) is a mature and superior sensing technology exploiting the statistical characteristic in the sample covariance matrix of the received signal [4]. The joint distribution of the matrix r in the hypothesis H1 can be expressed as r $ NðO; Rs þ RwÞ, in which Rs is the sample covariance matrix of s: Rs. 3.1 Stochastic resonance In order to recover the periodicity of the original signal furthest from intensive noise, the received signal in each antenna ri(i = 1, ..., M) will be processed via the SR system. The test statistics of the GLRT detector have some simple form expressed by the eigenvalue of the sample covariance matrix of the received signal. The algorithm steps are expressed as follows [5]: 1) Calculate the sample covariance matrix of filter output signal e(n) as: Re eðnÞeÃðnÞ: ð16Þ. TSR cannot reflect the time frequency, i.e., the actual execution time of the algorithm

Simulation results and discussion

Discussion

Conclusion