Traffic Modeling in Mobile Communication Networks

Osahenvemwen O.A., Edeko F.O., Emagbetere J.
2012 International Journal of Computer Applications  
This paper is focused on traffic modeling in Mobile Communication networks. This research is aimed at developing a traffic model that will predict a blocking probability for voice calls and handover calls blocking probability in mobile communication networks (GSM). The high number of block calls experience in mobile network, especially during the busy-hour as leads to poor Quality of Service (QOS) delivering in mobile network. The block calls experience in mobile network should be reduced (in
more » ... ne with NCC recommended value 2%) to a certain low values, to ensure good QOS. The developed traffic model is focused on new voice calls and handover calls in a cell. The developed traffic models are designed based on the number of channels resource available; these numbers of channels are partition into two segments in a cell network. The cell technology is homogenous in nature; therefore it is applicable to the entire mobile communication system. The analytical method is deployed, and the collection traffic data with equipment know as the Operation and Maintenance Center (OMC-counter) which is in built in the mobile communication network. The OMC-counter runs on Linux operation software, which helps to capture the number of arrival calls and service time in a specified interval. The arrival rate is assumed to be Poisson and the interarrival rate (the different between two arrival points or more) is also, assumed to be exponentially distributed and independence identical distributed. These parameters were assumed in the developed traffic model. The developed traffic models are blocking probability for voice calls and handover calls are shown in Equation ( 3 ) and ( 4 ). These traffic models are used to manage, a balance relationship between cost incurred in mobile communication by operators and service render to the mobile subscribers.
doi:10.5120/8069-1462 fatcat:ueb5ohu45zhmlh33gtipssxn7e