Hyper-Erlang distribution model and its application in wireless mobile networks
This paper presents the study of the hyper-Erlang distribution model and its applications in wireless networks and mobile computing systems. We demonstrate that the hyper-Erlang model provides a very general model for users' mobility and may provide a viable approximation to fat-tailed distribution which leads to the self-similar traffic. The significant difference from the traditional approach in the self-similarity study is that we want to provide an approximation model which preserves the Markovian property of the resulting queueing systems. We also illustrate that the hyper-Erlang distribution is a natural model for the characterization of the systems with mixed types of traffics. As an application, we apply the hyper-Erlang distribution to model the cell residence time (for users' mobility) and demonstrate the effect on channel holding time. This research may open a new avenue for traffic modeling and performance evaluation for future wireless networks and mobile computing systems, over which multiple types of services (voice, data or multimedia) will be supported.
wireless networks mobile computing channel holding times traffic modeling teletraffic pcs