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dc.contributor.advisorKwon, Hyuck M.
dc.contributor.authorZerngast, Andrew J.
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.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

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  • CE Theses and Dissertations
    Doctoral and Master's theses authored by the College of Engineering graduate students
  • EECS Theses and Dissertations
    Collection of Master's theses and Ph.D. dissertations completed at the Dept. of Electrical Engineering and Computer Science
  • Master's Theses
    This collection includes Master's theses completed at the Wichita State University Graduate School (Fall 2005 -- current) as well as selected historical theses.

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