{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/47105"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/47105","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Second-order Cyclostationary Feature Based Detection of WiMAX Signals in Pulsed Noise Environments","abstract":"Spectral coexistence and cooperative spectrum sharing techniques are vital to the continued development and proliferation of wireless communications systems. Government directives indicate that certain frequency bands which once were reserved for radar-only applications must now support wireless broadband systems. The effect of co-site interference upon detection techniques for wireless broadband systems is evaluated. Cyclostationary feature based detection methods are evaluated against gaussian noise and interfering radar signals. Alternative decision algorithms utilizing support vector machines are proposed and evaluated and compared against traditional general likelihood ratio test algorithms. Recommendations for certain algorithms and observation window lengths to maximize e ectiveness and minimize computational complexity are developed.","abstract_html":"Spectral coexistence and cooperative spectrum sharing techniques are vital to the continued development and proliferation of wireless communications systems. Government directives indicate that certain frequency bands which once were reserved for radar-only applications must now support wireless broadband systems. The effect of co-site interference upon detection techniques for wireless broadband systems is evaluated. Cyclostationary feature based detection methods are evaluated against gaussian noise and interfering radar signals. 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Government directives indicate that certain frequency bands which once were reserved for radar-only applications must now support wireless broadband systems. The effect of co-site interference upon detection techniques for wireless broadband systems is evaluated. Cyclostationary feature based detection methods are evaluated against gaussian noise and interfering radar signals. Alternative decision algorithms utilizing support vector machines are proposed and evaluated and compared against traditional general likelihood ratio test algorithms. 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