Title: Hidden Markov models for the burst error statistics of Viterbi decoding
Abstract:The method of the hidden Markov model (HMM) is used to develop a faithful model for the burst error statistics of Viterbi decoding of convolutional codes. One of the advantages of building such a mode...The method of the hidden Markov model (HMM) is used to develop a faithful model for the burst error statistics of Viterbi decoding of convolutional codes. One of the advantages of building such a model is that it can be used to generate the output sequence with little cost and can provide a basis for studying other system parameters. The HMM developed generally performs better than the geometric model and, in most cases, better than the previously proposed Markov model, and it requires much fewer parameters than those of the Markov model for convolutional codes of large constraint length.Read More
Publication Year: 1996
Publication Date: 1996-01-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 8
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