Modeling Poisson Error Process on Wireless Channels

Abstract

Burst error modeling has seen extensive research and progress over several decades evolving into ever more complex modeling techniques used today. This paper analyzed usefulness of the most prominent generative and descriptive (analytical) methods. Data containing error bits and packets from real wireless transmission has been used to obtain statistical information about error burst and gap behavior in the channel and various generative and descriptive modeling techniques were applied to model the error process with the goal of establishing advantages and disadvantages of each technique. Generative methods were represented by the commonly implemented Elliot’s model with parameters calculated using a generalized algebraic form and  descriptive methods were represented by one of the most flexible exponentially shaped distributions with regard to parameterization and heavy-tailed function modeling - gamma distribution, and lastly a technique represented by Markov modulated Poisson process (MMPP-2) producing second-order hyper-exponentially distributed characteristics. Results of the experiments were highly in favor of Elliot’s and MMPP-2 model demonstrating possible application of MMPP-2 model in application to commonly observed exponentially-shaped error process on the wireless channel.