Multiple-Antenna Cooperative Spectrum Sensing Based on the Wavelet Transform and Gaussian Mixture Model.

Shunchao Zhang,Hantao Yuan,Yongwei Zhang,Pin Wan,Yonghua Wang

doi:10.3390/s19183863

Shunchao Zhang, Hantao Yuan + Show 3 more

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https://doi.org/10.3390/s19183863

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Abstract

Spectrum sensing is a core technology in cognitive radio (CR) systems. In this paper, a multiple-antenna cooperative spectrum sensor based on the wavelet transform and Gaussian mixture model (MAWG) is proposed. Compared with traditional methods, the MAWG method avoids the derivation of the threshold and improves the performance of single secondary user (SU) spectrum sensing in cases of channel loss and hidden terminal. The MAWG method reduces the noise of the signal which collected by the multiple-antenna SUs through the wavelet transform. Then, the fusion center (FC) extracts the statistical features from the signals that are pre-processed by the wavelet transform. To extract the statistical features, an sensing data fusion method is proposed. The MAWG method divides all SUs that are involved in the cooperative spectrum sensing into two clusters and extracts a two-dimensional feature vector. In order to avoid complicated decision threshold derivation, the Gaussian mixture model (GMM) is used to train a classifier for spectrum sensing according to these two-dimensional feature vectors. Simulation experiments are performed in the channel model. The simulation shows that the MAWG can effectively improve spectrum sensing performance under the channel model.

Highlights

Spectrum sensing technology is used to detect and judge whether the primary user (PU) signal is present and find the spectrum holes for secondary users (SUs) to access [1,2,3]
Based on the current research, to improve the spectrum sensing performance in a fading environment, this paper proposes multiple-antenna cooperative spectrum sensing based on the wavelet transform and Gaussian mixture model (MAWG), which combines the advantages of cooperative SUs, multiple-antenna and clustering algorithm
Under a Rician channel, the performance is increased by 15.29%, 48.48%, and 145.00% respectively, when Pf = 0.1, and the performance is increased by 4.21%, 19.28%, and 65.00%, respectively, when Pf = 0.2

Summary

Introduction

Spectrum sensing technology is used to detect and judge whether the primary user (PU) signal is present and find the spectrum holes for secondary users (SUs) to access [1,2,3]. The single SU spectrum sensing methods are susceptible to channel fadding, hidden terminal and other issues To solve these shortcomings of classical spectrum sensing, random matrix theory (RMT) is applied to cooperative spectrum sensing (CSS), which has become a research hotspot [4]. In these methods, the covariance matrix should be calculated based on the signal matrix from SUs. the corresponding eigenvalue is calculated as a statistical feature of the covariance matrix for spectrum sensing. These methods need to derive threshold based on Sensors 2019, 19, 3863; doi:10.3390/s19183863 www.mdpi.com/journal/sensors

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