Hidden Markov models for the burst error statistics of Viterbi decoding

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.