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dc.contributor.advisorKwon, Hyuck M.
dc.contributor.authorZerngast, Andrew J.
dc.date.accessioned2011-09-02T20:04:59Z
dc.date.available2011-09-02T20:04:59Z
dc.date.copyright2010en
dc.date.issued2010-12
dc.identifier.othert10128
dc.identifier.urihttp://hdl.handle.net/10057/3757
dc.descriptionThesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science.en_US
dc.description.abstractThis thesis explores the bit error rate (BER) characteristics of a convolutional decoder constructed of a backpropagation neural network (NN) using a newly proposed input shifting window. Due to the fact that each NN is independent and unique, multiple NNs are placed in parallel and utilize the majority rule to further improve BER performance. NNs are efficient in complex statistical systems because of their inherently fast parallel-processing speed and pattern-recognition abilities. It was found that with a code rate of ½ and a constraint length of K = 3, a NN with the optimal convolutional code generator polynomial, which is non-systematic, has poor performance and a NN with a systematic convolutional code generator polynomial shows comparable performance to a conventional hard-decision Viterbi decoder using the optimal convolutional code generator polynomial.en_US
dc.format.extentviii, 24 leaves, ill.en
dc.language.isoen_USen_US
dc.publisherWichita State Universityen_US
dc.rightsCopyright Andrew J. Zerngast, 2010. All rights reserveden
dc.subject.lcshElectronic dissertationsen
dc.titleAn improved neural network-based decoder scheme for systematic convolutional codeen_US
dc.typeThesisen_US


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