文章基本信息

标题：Implementation of Machine Learning Spectrum Sensing for Cognitive Radio Applications
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作者：Mohamed El-Tarhuni ; Khaled Assaleh ; Firas Kiftaro 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2019
卷号：9
期号：4
页码：1-10
DOI：10.5121/csit.2019.90405
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：In this paper, a cognitive radio system is implemented using National Instruments (NI) Universal Software Radio Peripheral (USRP) devices. The implemented system provides a working prototype based on real data generated and collected by an experimental laboratory setup to compare the performance of spectrum sensing algorithms based on energy detection and polynomial classifier channel sensing techniques. For a sensing time interval ranging from 0.05 ms to 5ms, the experimental results show that the polynomial classifier has a better performance compared to the conventional energy detector in terms of the misclassification rate, especially at lower SNR values.
关键词：Cognitive Radio; Spectrum Dynamic Access; Spectrum Sensing; Polynomial Classifier; Energy Detection; USRP