{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/80935"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/80935","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Mitigating the Effects of Intersymbol Interference: Algorithms and Analysis","abstract":"In addition to asymptotic analysis of receivers for ISI channels, we also propose a joint maximum likelihood detection and decoding scheme for use when the channel is unknown to the receiver. Rather than employing training data to generate an estimate of the channel, the proposed receiver views the channel taps as stochastic quantities drawn from a known prior distribution and uses Bayesian techniques to compute estimates of the transmitted symbols. To implement the proposed receiver, we employ a stacklike algorithm, which estimates the transmitted bits by navigating the tree generated by the combined code and channel. We describe the derivation of the Bayesian metric and explore the performance loss incurred as a result of the lack of channel knowledge. In addition, we empirically characterize the robustness of the Bayesian detector to variations in the parameters of the prior distribution.","abstract_html":"In addition to asymptotic analysis of receivers for ISI channels, we also propose a joint maximum likelihood detection and decoding scheme for use when the channel is unknown to the receiver. Rather than employing training data to generate an estimate of the channel, the proposed receiver views the channel taps as stochastic quantities drawn from a known prior distribution and uses Bayesian techniques to compute estimates of the transmitted symbols. To implement the proposed receiver, we employ a stacklike algorithm, which estimates the transmitted bits by navigating the tree generated by the combined code and channel. We describe the derivation of the Bayesian metric and explore the performance loss incurred as a result of the lack of channel knowledge. In addition, we empirically characterize the robustness of the Bayesian detector to variations in the parameters of the prior distribution.","abstract_has_math":false,"creators":["Nelson, Jill Karen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Andrew Singer"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:08:54Z","date_published":"2015-09-25T20:08:54Z","updated_at":"2026-07-22T22:26:15Z","subjects":["Engineering, Electronics and Electrical"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3199099"],"render_values":[{"text":"(MiAaPQ)AAI3199099","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/80935","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Andrew Singer"]},{"key":"dc:creator","label":"Author","values":["Nelson, Jill Karen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:08:54Z","10000-01-01","2005"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering, Electronics and Electrical"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/80935","(MiAaPQ)AAI3199099"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In addition to asymptotic analysis of receivers for ISI channels, we also propose a joint maximum likelihood detection and decoding scheme for use when the channel is unknown to the receiver. 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Rather than employing training data to generate an estimate of the channel, the proposed receiver views the channel taps as stochastic quantities drawn from a known prior distribution and uses Bayesian techniques to compute estimates of the transmitted symbols. To implement the proposed receiver, we employ a stacklike algorithm, which estimates the transmitted bits by navigating the tree generated by the combined code and channel. We describe the derivation of the Bayesian metric and explore the performance loss incurred as a result of the lack of channel knowledge. In addition, we empirically characterize the robustness of the Bayesian detector to variations in the parameters of the prior distribution.","Made available in DSpace on 2015-09-25T20:08:54Z (GMT). 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