A neural strategy for optimal multiplexing of circuit- and packet-switched traffic

Hybrid fixed-length frames that are used to carry two basic traffic types, a circuit-switched isochronous and a packet-switched asynchronous one, are considered in a time-division multiplexing (TDM) structure. The available bandwidth, in terms of slots/frame, is dynamically allocated between the two traffic types, by means of a randomied decision rule, in order to minimize a cost function that takes into account circuit blocking probability and packet loss rate (for a finite packet buffer), or average packet delay (for an infinite buffer). The dynamics of the circuit-switched traffic can be represented by a discrete-time birth-death process, and the optimization problem is posed by considering the evolution of the corresponding Markov chain. The optimal decision rules over a finite future time horizon are approximated by a set of neural networks, whose parameters are determined by a method that exploits the finite state Markov chain model. Some preliminary numerical examples showing the convergence behavior of the parameters are reported