Effective assessment of mobile communication networks performance with clustering and neural modeling

In this paper, we present the core network model of universal mobile telecommunication system with calls that belong to one of four service classes and arrive randomly. Arriving calls are granted service based on specific service class, required maximum and minimum bandwidth, and available network resources. Performance of priority-based dynamic capacity allocation, suitable for the wireless ATM system is analyzed. Scheduling of the ATM cell transmission in each time division multiple access frame for the uplink is based on a priority scheme. Blocking probability and throughput parameters for bandwidth sharing policy are considered, and partial overlap link is implemented. The clustering procedure for the mobile communication networks performance assessment is developed where the blocking probability and throughput measurements are introduced with two-dimensional Markov reward model enhanced with vector quantification and neural modeling. The link occupancy probability distribution is optimized using neural network that was trained on the base of Kohonen rules. Simulation and numerical results are shown.