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

Zhang, Shunchao; Wang, Yonghua; Li, Jiangfan; Wan, Pin; Zhang, Yong‐Wei; Li, Nan

doi:10.1186/s13638-019-1338-z

Cited by 25 publications

(19 citation statements)

References 24 publications

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“…where e i ∈ R L×1 . In spectrum sensing, the detection probability, missed detection probability, and the false alarm probability are three key index to evaluate the performance of spectrum sensing schemes [20]. The detection probability is defined as…”

Section: A System Model Of Cognitive Radio Networkmentioning

confidence: 99%

Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

et al. 2020

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In a centralized cooperative spectrum sensing (CSS) system, it is vulnerable to malicious users (MUs) sending fraudulent sensing data, which can severely degrade the performance of CSS system. To solve this problem, we propose sensing data fusion schemes based on K-medoids and Mean-shift clustering algorithms to resist the MUs sending fraudulent sensing data in this paper. The cognitive users (CUs) send their local energy vector (EVs) to the fusion center which fuses these EVs as an EV with robustness by the proposed data fusion method. Specifically, this method takes a Medoids of all EVs as an initial value and searches for a high-density EV by iteratively as a representative statistical feature which is robust to malicious EVs from MUs. It does not need to distinguish MUs from CUs in the whole CSS process and considers constraints imposed by the CSS system such as the lack of information of PU and the number of MUs. Furthermore, we propose a global decision framework based on fast K-medoids or Mean-shift clustering algorithm, which is unaware of the distributions of primary user (PU) signal and environment noise. It is worth noting that this framework can avoid the derivation of threshold. The simulation results reflect the robustness of our proposed CSS scheme. INDEX TERMS Cognitive radio, robust cooperative spectrum sensing, sensing data fusion, K-medoids clustering algorithm, Mean-shift clustering algorithm.

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Section: A System Model Of Cognitive Radio Networkmentioning

confidence: 99%

Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

et al. 2020

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“…Figure 7 compares the sensing algorithm proposed in this paper with other sensing algorithms. The IG-FCM algorithm was proposed in [17], and the MME-Kmeans algorithm was proposed in [30]. In the figure, IG-DNN is in the range of SNR = −20 dB∼SNR = −14 dB, and the other two methods are SNR = −17 dB for the simulation experiments.…”

Section: Multi-component Signalmentioning

confidence: 99%

“…It used EMD to perform noise reduction processing and then used information geometry to perform feature extraction. A recent research work applied unsupervised learning to the classification of sensing signals [17]. This study combined information geometry and fuzzy C-means clustering algorithms to improve classification.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Sensing Method Based on Information Geometry and Deep Neural Network

Wan

Wang

et al. 2020

Entropy

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Due to the scarcity of radio spectrum resources and the growing demand, the use of spectrum sensing technology to improve the utilization of spectrum resources has become a hot research topic. In order to improve the utilization of spectrum resources, this paper proposes a spectrum sensing method that combines information geometry and deep learning. Firstly, the covariance matrix of the sensing signal is projected onto the statistical manifold. Each sensing signal can be regarded as a point on the manifold. Then, the geodesic distance between the signals is perceived as its statistical characteristics. Finally, deep neural network is used to classify the dataset composed of the geodesic distance. Simulation experiments show that the proposed spectrum sensing method based on deep neural network and information geometry has better performance in terms of sensing precision.

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“…There are some problems in cognitive radio networks (CRN), which are path losses and shadows. It is difficult for a single SU to accurately determine and judge whether the PU is using the licensed spectrum [24,25,26,27]. Therefore, in order to combat and reduce the impact of fading channels on spectrum sensing performance, this paper study cooperative SUs with multiple-antenna for spectrum sensing.…”

Section: Basic Multiple-antenna Css and Eigenvalues In Random Matrixmentioning

confidence: 99%

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

Zhang

Wang

Yuan

et al. 2019

Sensors

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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 κ - μ channel model. The simulation shows that the MAWG can effectively improve spectrum sensing performance under the κ - μ channel model.

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

Cited by 25 publications

References 24 publications

Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

Spectrum Sensing Method Based on Information Geometry and Deep Neural Network

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

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