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Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland


C. Steve Suh (editor)

Texas A&M University, USA


Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China


Performance Comparison of Cooperative Spectrum Sensing Models Based on Machine Learning

Journal of Vibration Testing and System Dynamics 7(2) (2023) 153--167 | DOI:10.5890/JVTSD.2023.06.004

Qian Hu, Zhong-Qiang Luo, Wen-Shi Xiao, Cheng-Jie Li

Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, 644000, China

School of Computer Science and Technology, Southwest Minzu University, Chengdu 610041, China

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So far machine learning (ML) has become increasingly significant in spectrum sensing (SS) for future intelligent wireless communications. In order to obtain an effective SS performance in fading channel, this paper proposes a GMM (Gaussian Mixture Model) based cooperative SS under the conditions of AWGN (Additive White Gaussian Noise) channel and Rayleigh flat fading channel. For evaluating the SS performance, numerous NB (Naive Bayes) and SVM (Support Vector Machine), MLP (Multi-Layer Perceptron) based ML method, as well as the traditional cooperative SS technologies (AND criterion, OR criterion, and Maximum Ration Combining (MRC)) are also investigated for performance comparison. The ROC (receiver operating characteristics) and AUC (area under the curve) performance index is used to compare performance. Simulation results and analysis show that the proposed GMM-based cooperative SS enable acquire the best performance under the Rayleigh channel.


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