Hidden Markov models for the burst error statistics of Viterbi decoding

The method of the hidden Markov model (HMM) is used to develop a Markov chain model for the burst error statistics of the Viterbi algorithm. Though only the case of Viterbi decoding is considered, the results presented can be applied to other applications of the Viterbi algorithm. Such a model can be used to generate the output sequence with little cost when compared with the real simulation of the Viterbi algorithm, and it provides a basis for studying other system parameters. The HMM developed generally performs better than the geometric model proposed by R. L. Miller et al. (1981) and in most cases better than the model by C.-C. Chao and R. J. McEliece (1990). It requires many fewer parameters than the model by Chao and McEliece for convolutional codes of large constraint length.<>