Double embedded processes based hidden Markov models for binary digital wireless channels

Generative models hold the promise of reducing the computational load and cost caused by directly simulating a real system. They are vital to the design and performance evaluation of error control schemes and high layer wireless communication protocols. Therefore, designing an efficient and accurate generative model is highly desirable. Moreover, the errors encountered in digital wireless channels exhibit correlation among them. This stimulates us to construct a Markovian based generative model with two embedded processes. The first process is dedicated to assembling error bursts with error-free bursts, whereas the second one is devoted to creating individual error bursts employing the maximum gap norm within error bursts. This premise is utilized in this paper to show that the resulting generative model can generate error sequences with desired bit correlations and is capable of statistically matching a descriptive model, derived from an enhanced general packet radio service (EGPRS) transmission system, regardless of the configuration of its error sequences.