@inproceedings{6b5aeddc84fb4601a10543d5521b1048,
title = "Spectrum sensing method via signal denoising based on sparse representation",
abstract = "he detection performance of traditional spectrum sensing with energy method in cognitive radio is restricted to the signal noise rate (SNR) of the received signal. The detection probability with constant false alarm rate decreases sharply because of the SNR wall. The sparse representation of signal can reveal the inherent property of the ideal signal. Using this feature, we can remove most of the noise added into the ideal signal. The sparse representation for denoising can be directly used if the sparse basis is known. If not, the K-SVD dictionary learning algorithm is adopted to build the sparse redundant dictionary. After denoising based on sparse representation, the noise energy falls down as well as the SNR increases greatly. As a consequence, compared with traditional energy detection, the sensing performance of new method improves apparently, especially, when the detection effect turns bad as the SNR falls down gradually.",
keywords = "Dictionary learning, K-SVD, Signal denoising, Sparse representation, Spectrum sensing",
author = "Yulong Gao and Youxiang Zhu and Yongkui Ma",
note = "Publisher Copyright: {\textcopyright} 2015 Taylor \& Francis Group, London.; International Symposium on Information Technology, ISIT 2014 ; Conference date: 14-10-2014 Through 16-10-2014",
year = "2015",
doi = "10.1201/b18776-66",
language = "英语",
isbn = "9781138027855",
series = "Proceedings of the 2014 International Symposium on Information Technology, ISIT 2014",
publisher = "CRC Press/Balkema",
pages = "349--354",
editor = "Yi Wan and Liangshan Shao and Jinguang Sun and Jingchang Nan and Quangui Zhang and Lipo Wang",
booktitle = "Proceedings of the 2014 International Symposium on Information Technology, ISIT 2014",
}