Primary User Signal Type Recognition Algorithm of Cognitive Radio Networks based on Active Learning in Building Indoors
- https://doi.org/10.2991/emcs-16.2016.274How to use a DOI?
- Active Learning; Primary User Signal Recognition; Support Vector Machine; Building Indoors; Spectral correlation analysis
Primary user signal modulation type recognition performance of building indoor environment has been the focus of attention and research in low signal-to-noise ratio. In this paper, a method based on active learning and support vector machine (SVM) for the primary user signal modulation type recognition is proposed in low signal to noise ratio. Three spectral coherence characteristic parameters are chosen via spectral correlation analysis, and the training samples and testing samples are formed for classification. Then, active learning algorithm is applied to obtain samples improved classification through a number of iterations, and SVM is formed. Finally, the formed SVM is utilized to recognize the primary user signal modulation type. Compared to the existing methods including the classifiers based on MME and ANN, the proposed approach is more effective in the case of low SNR and limited training numbers. The results show that the validity and superiority of the proposed algorithm on primary user signal modulation type recognition in building indoor environment.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xin Wang AU - Zhijun Gao AU - Fenglong Kan AU - Peng Guo AU - Yuanwei Qi AU - Jiuyi Lü AU - Hongwei Li AU - Bin Wang PY - 2016/01 DA - 2016/01 TI - Primary User Signal Type Recognition Algorithm of Cognitive Radio Networks based on Active Learning in Building Indoors BT - Proceedings of the 2016 International Conference on Education, Management, Computer and Society PB - Atlantis Press SP - 1115 EP - 1118 SN - 2352-538X UR - https://doi.org/10.2991/emcs-16.2016.274 DO - https://doi.org/10.2991/emcs-16.2016.274 ID - Wang2016/01 ER -