An improvement in spectrum usage is possible with the help of a cognitive radio network, which allows secondary users’ access to the unused licensed frequency band of a primary user. Thus, spectrum sensing is a fundamental concept in cognitive radio networks. In recent years, Cooperative spectrum sensing using machine learning has garnered a great deal of attention as a technique of enhancing sensing capability. In this study, K-means clustering is taken into consideration for the purpose of analyzing the effectiveness of cooperative spectrum sensing in a generalized α-κ-μ fading channel. The proposed approach is examined using receiver operating characteristic curves to determine its performance. The effectiveness of the proposed strategy is contrasted with that of the existing detection techniques such as Cooperating spectrum sensing based on energy detection and OR-fusion-based cooperative spectrum sensing for fading channels κ-μ, α-κ-μ. As demonstrated by results, the proposed method outshines an existing method in terms of comparison parameters, as determined by simulation results in the MATLAB version.

中文翻译：

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广义 α-κ-μ 衰落信道中基于机器学习的协同频谱感知

在认知无线电网络的帮助下，频谱使用的改善是可能的，它允许次要用户访问主要用户未使用的许可频段。因此，频谱感知是认知无线电网络中的一个基本概念。近年来，作为一种增强感知能力的技术，使用机器学习的协同频谱感知受到了广泛关注。在这项研究中，为了分析广义α-κ-μ衰落信道中协同频谱感知的有效性，考虑了K均值聚类。使用接收器操作特性曲线检查所提出的方法以确定其性能。将所提出的策略的有效性与现有检测技术的有效性进行了对比，例如基于能量检测的协作频谱感知和针对衰落信道κ-μ，α-κ-μ的基于OR-融合的协作频谱感知。正如结果所证明的那样，所提出的方法在比较参数方面优于现有方法，这是由 MATLAB 版本中的仿真结果确定的。