TRAFFIC MODELS FOR WIRELESS COMMUNICATION-NETWORKS

In this paper, we introduce a deterministic fluid model and two stocha stic traffic models for wireless networks. The setting is a highway wi th multiple entrances and exits. Vehicles are classified as calling or noncalling, depending upon whether or not they have calls in progress . The main interest is in the calling vehicles, but noncalling vehicle s are important because they can become calling vehicles if they initi ate (place or receive) a call. The deterministic model ignores the beh avior of individual vehicles and treats them as a continuous fluid, wh ereas the stochastic traffic models consider the random behavior of ea ch vehicle. However, all three models use the same two coupled partial differential equations (PDE's) or ordinary differential equations (OD E's) to describe the evolution of the system. The call density and cal l handoff rate (or their expected values in the stochastic models) are readily computable by solving these equations. Since no capacity cons traints are imposed in the models, these computed quantities can be re garded as offered traffic loads. The models complement each other, bec ause the fluid model can be extended to include additional features su ch as capacity constraints and the interdependence between velocity an d vehicular density, while the stochastic traffic model can provide pr obability distributions. Numerical examples are presented to illustrat e how the models can be used to investigate various aspects of time an d space dynamics in wireless networks. The numerical results indicate that both the time-dependence and the mobility of vehicles can play im portant roles in determining system performance. Even for systems in s teady state with respect to time, the movement of vehicles and the cal ling patterns can significantly affect the number of calls in a given region of the system. The examples demonstrate that the proposed model s can serve as useful tools for system engineering and planning. For i nstance, we calculate approximate call blocking probabilities.