Neural decision making for decentralized pricing-based call admission control
Franco Davoli, Mario Marchese, Maurizio Mongelli · 2005
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.