A multiplexing structure is considered, where TDM frames are used to carry both isochronous, circuit-switched, and asynchronous, packet-switched traffic. Control functions are sought, whose task is that of deciding the allocation of the frame capacity between the two traffic types. The problem is defined in the context of Markov Decision Processes, and multilayer feedforward neural networks are used to approximate the optimal control laws. A backpropagation algorithm is described, which exploits the finiteness of the system's state. The procedure is conceived in the framework of repetitive (receding horizon) control schemes. Numerical results are presented, as well as comparisons with the control laws obtained by applying a dynamic programming algorithm.
Neural approximations of optimal allocation policies for hybrid multiplexing
BOLLA, RAFFAELE;DAVOLI, FRANCO;
1995-01-01
Abstract
A multiplexing structure is considered, where TDM frames are used to carry both isochronous, circuit-switched, and asynchronous, packet-switched traffic. Control functions are sought, whose task is that of deciding the allocation of the frame capacity between the two traffic types. The problem is defined in the context of Markov Decision Processes, and multilayer feedforward neural networks are used to approximate the optimal control laws. A backpropagation algorithm is described, which exploits the finiteness of the system's state. The procedure is conceived in the framework of repetitive (receding horizon) control schemes. Numerical results are presented, as well as comparisons with the control laws obtained by applying a dynamic programming algorithm.
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