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Lajnef and M.C. Dakhli, \u201cThe effect of the nonlinear distortion on OFDM based cognitive radio systems over Rayleigh fading channel,\u201d 2016 30th International Conference on Advanced Information Networking and Applications Workshops, pp.427-430, March 2016. 10.1109\/waina.2016.47","DOI":"10.1109\/WAINA.2016.47"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] S. Ren, Z. Zeng, C. Guo, X. Sun, and K. Su, \u201cA low computational complexity algorithm for compressive wideband spectrum sensing,\u201d IEICE Trans. Fundamntal., vol.E100-A, no.1 pp.294-300, Jan. 2017. 10.1587\/transfun.e100.a.294","DOI":"10.1587\/transfun.E100.A.294"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] J. Meng, W. Yin, and Y. Li, \u201cCompressive sensing based high resolution channel estimation for OFDM system,\u201d IEEE J. Sel. 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Zhang, \u201cSensing performance of efficient cyclostationary detector with multiple antennas in multipath fading and lognormal shadowing environments,\u201d J. Commun. Netw., vol.16, no.2, pp.162-171, May 2014. 10.1109\/jcn.2014.000027","DOI":"10.1109\/JCN.2014.000027"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] E. Axell, \u201cSpectrum sensing for cognitive radio: State-of-the-art and recent advances,\u201d IEEE Signal Process. Mag, vol.29, no.3, pp.1-38, 2012. 10.1109\/msp.2012.2183771","DOI":"10.1109\/MSP.2012.2183771"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] S. Chaudhari, V. Koivunen, and H.V. Poor, \u201cAutocorrelation-based decentralized sequential detection of OFDM signals in cognitive radio,\u201d IEEE Trans. Signal Process., vol.57, no.7, pp.2690-2700, March 2009. 10.1109\/tsp.2009.2019176","DOI":"10.1109\/TSP.2009.2019176"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] L. Zhou, \u201cDetection of the guard interval length in OFDM systems,\u201d IEEE Consumer Communication and Networking, vol.2, no.2, pp.1048-1051, Jan. 2006. 10.1109\/ccnc.2006.1593198","DOI":"10.1109\/CCNC.2006.1593198"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] E. Axell and E.G. Larsson, \u201cOptimal and sub-optimal spectrum sensing of OFDM signals in known and unknown noise variance,\u201d IEEE J. Sel. Areas Commun., vol.29, no.2, pp.290-304, Jan. 2011. 10.1109\/jsac.2011.110203","DOI":"10.1109\/JSAC.2011.110203"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] M.H. Ibrahim, M.A. Usman, and S.Y. Shin, \u201cA cascaded approach to efficient detection of OFDM signals based on energy and autocorrelation detection,\u201d Can. J. Electr. Comput. Eng., vol.29, no.2, pp.235-241, 2016. 10.1109\/cjece.2016.2547991","DOI":"10.1109\/CJECE.2016.2547991"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] S.A. Razavi, M. Valkama, and D. Cabric, \u201cCovariance-based OFDM spectrum sensing with sub-Nyquist samples,\u201d Signal Process., vol.109, no.2, pp.261-268, 2015. 10.1016\/j.sigpro.2014.11.017","DOI":"10.1016\/j.sigpro.2014.11.017"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] M. Zhang, L. Wang, and L. Ding, \u201cA fast spectrum sensing for OFDM based on adaptive thresholding,\u201d Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp.25-26, March 2017. 10.1109\/iaeac.2017.8054511","DOI":"10.1109\/IAEAC.2017.8054511"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] P. Zhong and Z. Gong, \u201cA hybrid DBN and CRF Model for spectral-spatial classification of hyperspectral images,\u201d Stat., Optim. Inf. Comput., vol.5, no.2, pp.75-98, 2017. 10.19139\/soic.v5i2.309","DOI":"10.19139\/soic.v5i2.309"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] V. Gatla, M. Venkatesan, and A. 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Han, \u201cSVM-based detection in visible light communications,\u201d Optik-International Journal for Light and Electron Optics, vol.151, pp.55-64, 2017. 10.1016\/j.ijleo.2017.08.089","DOI":"10.1016\/j.ijleo.2017.08.089"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] A. Ali, F. Yangyu, and S. Liu, \u201cAutomatic modulation classification of digital modulation signals with stacked autoencoders,\u201d Digit. Signal Process., vol.71, pp.108-116, 2017. 10.1016\/j.dsp.2017.09.005","DOI":"10.1016\/j.dsp.2017.09.005"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] G.J. Mendis, J. Wei, and A. Madanayake, \u201cDeep learning-based automated modulation classification for cognitive radio,\u201d IEEE ICCS, pp.1-6, 2016 10.1109\/iccs.2016.7833571","DOI":"10.1109\/ICCS.2016.7833571"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] S. Lin, L. Cai, X. Lin, and R. 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