Modeling Poisson Error Process on Wireless Channels

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 some prominent generative and descriptive (analytical) methods. Data containing error bits and packets from real wireless transmission was captured on a physical interface and used to obtain statistical information about error burst and gap behavior in the channel. Generative and descriptive modeling techniques were then 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. Descriptive methods were represented by 2 models; one of the most flexible exponentially shaped distributions with regard to parameterization and heavy-tailed function modeling gamma distribution model, and a model utilizing a novel parameterization approach for the Markov modulated Poisson process (MMPP-2), producing second-order hyper-exponentially distributed characteristics. Results of the experiments were highly in favor of MMPP-2 model using a novel parameterization approach, demonstrating capability of MMPP-2 to model heavily interfered wireless channels exhibiting exponentially-shaped error.