Neural decision making for decentralized pricing-based call admission control

In this paper, a novel call admission control (CAC) problem is investigated in relation to the pricing structure of a telecommunication network, in which both guaranteed performance (GP) and best effort (BE) services are offered. The user´s sensitivity to the prices is described through utility functions. An original decision making process is studied to decentralize the proposed CAC mechanism. To this aim, a neural approximation technique is investigated to exploit different decision makers, distributed in the network and performing the CAC decisions. Simulation results show how sub-optimal CAC decisions are obtained in a decentralized fashion and with a small on-line computational effort.