A cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm

Shunchao Zhang,Nan Li,Jiangfan Li,Yonghua Wang,Yongwei Zhang,Pin Wan

doi:10.1186/s13638-019-1338-z

Abstract

To improve spectrum sensing performance, a cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm is proposed in this paper. In the process of signal feature extraction, a feature extraction method combining decomposition, recombination, and information geometry is proposed. First, to improve the spectrum sensing performance when the number of cooperative secondary users is small, the signals collected by the secondary users are split and reorganized, thereby logically increasing the number of cooperative secondary users. Then, in order to visually analyze the signal detection problem, the information geometry theory is used to map the split and recombine signals onto the manifold, thereby transforming the signal detection problem into a geometric problem. Further, use geometric tools to extract the corresponding statistical characteristics of the signal. Finally, according to the extracted features, the appropriate classifier is trained by the fuzzy c-means clustering algorithm and used for spectrum sensing, thus avoiding complex threshold derivation. In the simulation results and performance analysis section, the experimental results were further analyzed, and the results show that the proposed method can effectively improve the spectrum sensing performance.

Highlights

With the development of wireless communication, spectrum resources have become increasingly scarce, but most of the existing spectrum resources have not been fully utilized
1.2 Contributions Based on the above researches, this paper proposes a cooperative spectrum sensing method based on information geometry and fuzzy c-means (FCM) clustering algorithm (IGFCM)
Notations Gaussian noise The signal transmitted by the primary user (PU) The signal received by the Secondary user (SU) The number of sampling points The number of SUs participating in CSS PU exists, PU does not exist False alarm probability and detection probability The signal acquired by the ith SU Signal matrix Covariance matrix corresponding to X The transposition of X A matrix after O-decomposition and recombination (DAR) and I-DAR Covariance matrix corresponding to YO−DAR

Summary

Introduction

With the development of wireless communication, spectrum resources have become increasingly scarce, but most of the existing spectrum resources have not been fully utilized. The modified method uses the extracted MME feature value as a statistical feature of the signal and compares with a preset threshold to determine whether the PU exists [11]. Similar to the above method, the DMM feature is used as a statistical feature, and spectrum sensing is implemented by comparing with a preset threshold This method has poor perceived performance when the number of cooperative SUs is small and the signal-to-noise ratio (SNR) is low. Zhang et al proposed a spectrum sensing method based on K-means and signal features that combines the feature extraction methods in the random matrix and selects MME, DMM, and DMEAE as the characteristics of training and classification [22]. 1.2 Contributions Based on the above researches, this paper proposes a cooperative spectrum sensing method based on information geometry and fuzzy c-means (FCM) clustering algorithm (IGFCM).

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Cooperative spectrum sensing based on FCM clustering algorithm

Conclusion