Performance Comparison of Cooperative Spectrum Sensing Models Based on Machine Learning - Hu, Qian; Luo, Zhong-Qiang; Xiao, Wen-Shi; Li, Cheng-Jie (Journal of Vibration Testing and System Dynamics, 2026)
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.