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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link=blue vlink=purple><div class=WordSection1><div><p class=MsoNormal><o:p> </o:p></p></div><table class=MsoNormalTable border=0 cellpadding=0><tr><td style='padding:.75pt .75pt .75pt .75pt'><div><p class=MsoNormal align=center style='mso-margin-top-alt:auto;margin-bottom:10.0pt;text-align:center'><span style='font-size:10.5pt;font-family:"Calibri","sans-serif";background:#F5F8F0'> </span><b><span style='font-size:18.0pt;font-family:"Calibri","sans-serif"'>Wideband Spectrum Sensing for Cognitive Radios</span></b><o:p></o:p></p><p class=MsoNormal align=center style='mso-margin-top-alt:auto;margin-bottom:10.0pt;text-align:center'><span style='font-size:10.5pt;font-family:"Calibri","sans-serif";background:#F5F8F0'>> </span><b><span style='font-family:"Calibri","sans-serif"'>Speaker: Zhi Tian, Michigan Technological University</span></b><o:p></o:p></p><p class=MsoNormal align=center style='mso-margin-top-alt:auto;margin-bottom:10.0pt;text-align:center'><span style='font-size:10.5pt;font-family:"Calibri","sans-serif";background:#F5F8F0'>> </span><b><span style='font-family:"Calibri","sans-serif"'>Host: Zhu Han</span></b><o:p></o:p></p><p class=MsoNormal align=center style='mso-margin-top-alt:auto;margin-bottom:10.0pt;text-align:center'><span style='font-size:10.5pt;font-family:"Calibri","sans-serif";background:#F5F8F0'>> </span><span style='font-family:"Calibri","sans-serif"'>D102 Engineering Building I, 2/18, 10am</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;margin-bottom:10.0pt'><span style='font-size:10.5pt;font-family:"Calibri","sans-serif";background:#F5F8F0'>> </span><span style='font-family:"Calibri","sans-serif"'>Abstract: </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;margin-bottom:10.0pt;text-align:justify'><span style='font-size:10.5pt;font-family:"Calibri","sans-serif";background:#F5F8F0'>> </span><span style='font-family:"Calibri","sans-serif"'>Dynamic spectrum access has emerged as a promising paradigm to improve the spectrum utilization of wireless networks. Key to this paradigm are frequency-agile cognitive radios (CRs) that are aware of the radio environment and can dynamically program their parameters to efficiently utilize vacant spectrum without causing harmful interference to authorized primary users. The unprecedented radio agility envisioned, calls for fast and accurate spectrum sensing over very wide bandwidth, which renders traditional spectral estimation methods inefficient due to their high signal-acquisition costs in sampling at or above the Nyquist rate.</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;margin-bottom:10.0pt;text-align:justify'><span style='font-size:10.5pt;font-family:"Calibri","sans-serif";background:#F5F8F0'>> </span><span style='font-family:"Calibri","sans-serif"'>In this talk, we will present efficient spectrum sensing techniques that are tailored to the distinct nature of CR sensing. Recognizing the signal sparsity induced by low spectrum utilization in current wireless networks, we develop compressed sensing solutions that can effectively reconstruct the wide spectrum from a small number of samples collected at sub-Nyquist rates. In particular, we present a compressed sensing approach to cyclic feature based signal detection and modulation classification. Wideband communication signals possess unique two-dimensional sparsity structures in both the frequency domain and the modulation-dependent cyclic frequency domain. Exploitation of these sparsity elements not only reveals important features of the modulated signals, but also results in fast reconstruction of the cyclic statistics and hence reduced sensing time. The resultant compressive cyclic feature detector is able to simultaneously estimate the spectrum occupancy of multiple narrowband and wideband signals occupying the wide frequency band, and at the same time mitigate non-cyclic noise and interference. </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;margin-bottom:10.0pt;text-align:justify'><span style='font-size:10.5pt;font-family:"Calibri","sans-serif";background:#F5F8F0'>> </span><span style='font-family:"Calibri","sans-serif"'> </span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;margin-bottom:10.0pt;text-align:justify'><span style='font-size:10.5pt;font-family:"Calibri","sans-serif";background:#F5F8F0'>> </span><span style='font-family:"Calibri","sans-serif"'>Biography:</span><o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;margin-bottom:10.0pt;text-align:justify'><span style='font-size:10.5pt;font-family:"Calibri","sans-serif";background:#F5F8F0'>> </span><span style='font-family:"Calibri","sans-serif"'>Zhi (Gerry) Tian is an Associate Professor at the department of Electrical and Computer Engineering, Michigan Tech University. Her general interest lies in signal processing for communications. Current research focuses on cognitive radio networks and distributed wireless sensor networking. She served as Associate Editor for IEEE Transactions on Wireless Communications and IEEE Transaction on Signal Processing. She received a CAREER award from the US National Science Foundation in 2003.</span><o:p></o:p></p><p><span style='font-size:10.5pt;background:#F5F8F0'>> </span> <o:p></o:p></p></div></td></tr></table><div><p class=MsoNormal><o:p> </o:p></p></div></div></body></html>