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dc.contributor.authorSteiner, Michael Paulen
dc.date.accessioned2014-03-14T20:34:56Zen
dc.date.available2014-03-14T20:34:56Zen
dc.date.issued2011-04-28en
dc.identifier.otheretd-05032011-133905en
dc.identifier.urihttp://hdl.handle.net/10919/32161en
dc.description.abstractThis paper describes the implementation of a scanning signal detector and automatic modulation classification system. The classification technique is a completely blind method, with no prior knowledge of the signalâ s center frequency, bandwidth, or symbol rate. An energy detector forms the initial approximations of the signal parameters. The energy detector used in the wideband sweep is reused to obtain fine estimates of the center frequency and bandwidth of the signal. The subsequent steps reduce the effect of frequency offset and sample timing error, resulting in a constellation of the modulation of interest. The cumulant of the constellation is compared to a set of known ideal cumulant values, forming the classification estimate. The algorithm uses two platforms that together provide high speed parallel processing and flexible run-time operation. High-rate spectral scanning using an energy detector is run in parallel with a variable down sampling path; both are highly pipelined structures, which allows for high data throughput. A pair of processing cores is used to record spectral usage and signal characteristics as well as perform the actual classification. The resulting classification system can accurately identify modulations below 5 dB of signal-to-noise ratio (SNR) for some cases of the phase shift keying family of modulations but requires a much higher SNR to accurately classify higher-order modulations. These estimates tend toward classifying all signals as binary phase shift keying because of limits of the noise power estimation part of the cumulant normalization process. Other effects due to frequency offset and synchronization timing are discussed.en
dc.publisherVirginia Techen
dc.relation.haspartSteiner_MP_T_2011v2.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectimplementationen
dc.subjectblind classificationen
dc.subjectcumulanten
dc.subjectmodulation classificaitonen
dc.subjectreal-timeen
dc.subjectspectrum sensingen
dc.titleSpectrum Sensing and Blind Automatic Modulation Classification in Real-Timeen
dc.typeThesisen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.description.degreeMaster of Scienceen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelmastersen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineElectrical and Computer Engineeringen
dc.contributor.committeechairBose, Tamalen
dc.contributor.committeememberHasan, S. M. Shajedulen
dc.contributor.committeememberReed, Jeffrey H.en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05032011-133905/en
dc.date.sdate2011-05-03en
dc.date.rdate2011-06-13en
dc.date.adate2011-06-13en


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