Cellular neural network approach to a class of communication problems
Romano Fantacci, Mauro Forti, Mauro Marini, L. Pancani · IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications · 1999
In this paper we discuss the design of a cellular neural network (CNN) to solve a class of optimization problems of importance for communication networks. The CNN optimization capabilities are exploited to implement an efficient cell scheduling algorithm in a fast packet switching fabric. The neural-based switching fabric maximizes the cell throughput and, at the same time, it is able to meet a variety of quality of service (QoS) requirements by optimizing a suitable function of the switching delay and priority of the cells. We also show that the CNN approach has advantages with respect to that based on Hopfield neural networks (HNNs) to solve the considered class of optimization problems. In particular, we exploit existing techniques to design CNNs with a prescribed set of stable binary equilibrium points as a basic tool to suppress spurious responses and, hence to optimize the neural switching fabric performance.