| Citation: | Bin SHEN, Xin WANG, Siji CHEN, Taiping CUI. Machine Learning Based Primary User Transmit Mode Classification for Spectrum Sensing in Cellular Cognitive Radio Network[J]. Journal of Electronics & Information Technology, 2021, 43(1): 92-100. doi: 10.11999/JEIT191012
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In recent years, Machine Learning (ML) based spectrum sensing technology has provided a new solution in spectrum status identification for cognitive radio systems. Based on the large amount of spectrum observations captured by the Secondary User Equipment (SUE) in the Cellular Cognitive Radio Network (CCRN), this paper proposes a spectrum sensing scheme based on the Primary User (PU) transmission mode classification. Firstly, based on a variety of typical ML classification algorithms, the proposed scheme classifies the transmission mode of multiple Primary User Transmitters (PUTs) in the CCRN, and determines the joint operating state of all the PUTs in the CCRN. Subsequently, the SUE evaluates the possibility of accessing the licensed spectrum in the currently determined PUT transmission mode according to its geographical location or spectrum observation data. Since the actual locations of the PUTs in the network may be readily known in advance or unaware of at all, the proposed scheme solves the problem in three different methods. Theoretical derivation and experimental results show that compared with the traditional energy detection scheme, the proposed scheme not only remarkably improves the spectrum sensing performance, but also significantly increases the opportunities of dynamic accessing to the licensed spectrum for the SUEs. The proposed scheme can be used as an efficient and practical spectrum sensing solution in the CCRN.
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