Research Article
LRS- Based Non-parametric Spectrum Sensing for Cognitive Radio
@INPROCEEDINGS{10.1007/978-3-319-40352-6_27, author={D. Patel and Y. Trivedi}, title={LRS- Based Non-parametric Spectrum Sensing for Cognitive Radio}, proceedings={Cognitive Radio Oriented Wireless Networks. 11th International Conference, CROWNCOM 2016, Grenoble, France, May 30 - June 1, 2016, Proceedings}, proceedings_a={CROWNCOM}, year={2016}, month={6}, keywords={Spectrum sensing Goodness of fit test Likelihood ratio statistic Noise uncertainty Time-varying channel (AR1)}, doi={10.1007/978-3-319-40352-6_27} }- D. Patel
Y. Trivedi
Year: 2016
LRS- Based Non-parametric Spectrum Sensing for Cognitive Radio
CROWNCOM
Springer
DOI: 10.1007/978-3-319-40352-6_27
Abstract
In this paper, a novel non-parametric spectrum sensing scheme in cognitive radio (CR) is proposed based on robust Goodness of Fit (GoF) test. The proposed scheme uses likelihood ratio statistics (LRS-), from which goodness of fit test is derived. The test is applied assuming different types of primary user (PU) signals such as static or constant, single frequency sine wave and Gaussian signals, whereas different types of channels such as additive white Gaussian noise (AWGN), block fading and time-varying channels. Considering a real time scenario, uncertainty in noise variance is also assumed. The performance of the proposed scheme is shown using receiver operating characteristics (ROC) and it is compared with energy detection (ED) and prevailing GoF based sensing techniques such as Anderson-Darling (AD) sensing, Order Statistic based sensing and Kolmogrov-Smirnov (KS) sensing. It is shown that the proposed scheme outperforms all these prevailing schemes.