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dc.contributor.authorMohammad, Maruf H.en_US
dc.date.accessioned2014-03-14T21:51:22Z
dc.date.available2014-03-14T21:51:22Z
dc.date.issued2002-11-26en_US
dc.identifier.otheretd-12122002-150624en_US
dc.identifier.urihttp://hdl.handle.net/10919/46193
dc.description.abstractBlind Acquisition of Short Burst with Per-Survivor Processing (PSP) Maruf Mohammad (Abstract) This thesis investigates the use of Maximum Likelihood Sequence Estimation (MLSE) in the presence of unknown channel parameters. MLSE is a fundamental problem that is closely related to many modern research areas like Space-Time Coding, Overloaded Array Processing and Multi-User Detection. Per-Survivor Processing (PSP) is a technique for approximating MLSE for unknown channels by embedding channel estimation into the structure of the Viterbi Algorithm (VA). In the case of successful acquisition, the convergence rate of PSP is comparable to that of the pilot-aided RLS algorithm. However, the performance of PSP degrades when certain sequences are transmitted. In this thesis, the blind acquisition characteristics of PSP are discussed. The problematic sequences for any joint ML data and channel estimator are discussed from an analytic perspective. Based on the theory of indistinguishable sequences, modifications to conventional PSP are suggested that improve its acquisition performance significantly. The effect of tree search and list-based algorithms on PSP is also discussed. Proposed improvement techniques are compared for different channels. For higher order channels, complexity issues dominate the choice of algorithms, so PSP with state reduction techniques is considered. Typical misacquisition conditions, transients, and initialization issues are reported.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartThesis.PDFen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectMaximum likelihood (ML) detectionen_US
dc.subjectBlind channel estimationen_US
dc.subjectDDFSEen_US
dc.subjectPer-Survivor Processing (PSP)en_US
dc.subjectViterbi Algorithmen_US
dc.subjectM-algorithm.en_US
dc.titleBlind Acquisition of Short Burst with Per-Survivor Processing (PSP)en_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
dc.contributor.committeechairTranter, William H.en_US
dc.contributor.committeememberWoerner, Brain D.en_US
dc.contributor.committeememberReed, Jeffrey Hughen_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-12122002-150624/en_US
dc.date.sdate2002-12-12en_US
dc.date.rdate2003-12-13
dc.date.adate2002-12-13en_US


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