Post-Error Correcting Code Modeling of Burst Channels Using Hidden Markov Models With Applications to Magnetic Recording

We present two approaches for modeling burst channels using hidden Markov models (HMMs). The first method is based on the maximum-likelihood approach and improves on the computational efficiency of earlier methods. We present new algorithms for scaling and for determining the model parameters by using smart search techniques. We then generalize a gap length analysis and apply it to modeling HMMs. The algorithms are low-complexity and memory-efficient. Finally, we present simulation results for modeling errors in magnetic storage channels and show how this can be used for evaluating decoder failure rates by using Wolf's method, from real observed data