Master of Sciences and Information Technologies, Metropolitan Autonomous University Iztapalapa, Mexico City 09360, Mexico.
Electrical Engineering Department, Metropolitan Autonomous University Iztapalapa, Mexico City 09360, Mexico.
Sensors (Basel). 2019 Oct 30;19(21):4715. doi: 10.3390/s19214715.
In this work, three specific machine learning techniques (neural networks, expectation maximization and -means) are applied to a multiband spectrum sensing technique for cognitive radios. All of them have been used as a classifier using the approximation coefficients from a Multiresolution Analysis in order to detect presence of one or multiple primary users in a wideband spectrum. Methods were tested on simulated and real signals showing a good performance. The results presented of these three methods are effective options for detecting primary user transmission on the multiband spectrum. These methodologies work for 99% of cases under simulated signals of SNR higher than 0 dB and are feasible in the case of real signals.
在这项工作中，三种特定的机器学习技术（神经网络、期望最大化和 -means）被应用于认知无线电的多频带频谱感知技术。它们都被用作分类器，使用多分辨率分析的逼近系数来检测宽带频谱中一个或多个主用户的存在。这些方法在模拟和真实信号上进行了测试，表现出了良好的性能。这三种方法的结果表明，它们是检测多频带频谱中主用户传输的有效选择。在 SNR 高于 0dB 的模拟信号下，这些方法在 99%的情况下都有效，在真实信号的情况下也是可行的。
