Spectrum Sensing and Blind Automatic Modulation Classification in Real-Time
| dc.contributor.author | Steiner, Michael Paul | en |
| dc.contributor.committeechair | Bose, Tamal | en |
| dc.contributor.committeemember | Hasan, S. M. Shajedul | en |
| dc.contributor.committeemember | Reed, Jeffrey H. | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2014-03-14T20:34:56Z | en |
| dc.date.adate | 2011-06-13 | en |
| dc.date.available | 2014-03-14T20:34:56Z | en |
| dc.date.issued | 2011-04-28 | en |
| dc.date.rdate | 2011-06-13 | en |
| dc.date.sdate | 2011-05-03 | en |
| dc.description.abstract | This 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 signal'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.description.degree | Master of Science | en |
| dc.identifier.other | etd-05032011-133905 | en |
| dc.identifier.sourceurl | http://scholar.lib.vt.edu/theses/available/etd-05032011-133905/ | en |
| dc.identifier.uri | http://hdl.handle.net/10919/32161 | en |
| dc.publisher | Virginia Tech | en |
| dc.relation.haspart | Steiner_MP_T_2011v2.pdf | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | implementation | en |
| dc.subject | blind classification | en |
| dc.subject | cumulant | en |
| dc.subject | modulation classification | en |
| dc.subject | real-time | en |
| dc.subject | spectrum sensing | en |
| dc.title | Spectrum Sensing and Blind Automatic Modulation Classification in Real-Time | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Electrical and Computer Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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