Modeling of bursty channels using stochastic context-free grammars

In order to design good error control schemes for bursty channels, and also to facilitate performance analysis, it is important to develop accurate and simple statistical models for the channels of interest. We propose a novel method, based on stochastic context-free grammars, to model channels described by long well-defined error bursts interleaved with longer error-free intervals. Compared with previous approaches based on Markov chains, the proposed model achieves a much better performance with similar training complexity. It also outperforms specific methods based on hidden Markov models developed for the same type of bursty channels, with the additional advantage of requiring much less training computation.