Iterative Decoding of K/N Convolutional Codes based on Recurrent Neural Network with Stopping Criterion

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dc.contributor.author Kao, Johnny W. H en_AU
dc.contributor.author Berber, Stevan M. en_AU
dc.date.accessioned 2007-03-12T22:05:29Z
dc.date.accessioned 2012-12-15T02:29:40Z
dc.date.available 2007-03-12T22:05:29Z
dc.date.available 2012-12-15T02:29:40Z
dc.date.issued 2007-03-12T22:05:29Z
dc.identifier.uri http://hdl.handle.net/2100/70
dc.identifier.uri http://hdl.handle.net/10453/19630
dc.description.abstract This paper outlines a novel iterative decoding technique for a rate K/N convolutional code based on recurrent neural network (RNN) with stopping criterion. The algorithm is introduced by describing the theoretical models of the encoder and decoder. In particular this paper focuses on the investigation of a stopping criterion on the iterating procedure in order to minimize the decoding time yet still obtain an optimal BER performance. The simulation results of a rate 1/2 and 2/3 encoders respectively in comparison with the conventional Viterbi decoder are also presented en_AU
dc.format.extent 130910 bytes
dc.format.mimetype application/pdf
dc.language.iso en_AU
dc.title Iterative Decoding of K/N Convolutional Codes based on Recurrent Neural Network with Stopping Criterion en_AU


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