In this study, the authors consider the application of machine learning (ML) models in cooperative spectrum sensing of cognitive radio networks (CRNs). Based on a statistical analysis of the classic energy detection scheme, the probability of detection and false alarm is derived, which depends solely on the number of samples and signal-to-noise ratio of the secondary users. The channel occupancy detection obtained from the established analytical techniques such as maximum ratio combining and AND/OR rules is compared to different ML techniques, including multilayer perceptron (MLP), support vector machine, and Naive Bayes, based on receiver operating characteristic and area under the curve metrics. By using standard profiling tools, they obtain the computational performance of the analysed models during the training phase, a critical step for operating in CRNs. Ultimately, the results demonstrate that the MLP ML technique presents a better trade-off between training time and channel detection performance.

中文翻译：

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基于机器学习的协作无线电网络中的频谱感知模型

在这项研究中，作者考虑了机器学习（ML）模型在认知无线电网络（CRN）的协作频谱感知中的应用。在对经典能量检测方案进行统计分析的基础上，得出了检测和错误警报的概率，这完全取决于二次用户的样本数量和信噪比。根据接收器的工作特性和下方区域，将从已建立的分析技术（例如最大比率合并和与/或规则）获得的信道占用检测与不同的ML技术（包括多层感知器（MLP），支持向量机和朴素贝叶斯）进行比较曲线指标。通过使用标准配置工具，他们可以在训练阶段获得分析模型的计算性能，在CRN中运行的关键步骤。最终，结果表明，MLP ML技术在训练时间和信道检测性能之间表现出更好的折衷。