Recent developments of Internet services and advanced compression methods has revived interest on IP based multimedia satellite communication systems. However a main problem arising here is to guarantee specific Quality of Service (QoS) constraints in order to have good performance for each traffic class. Among various QoS approach used in Internet, recently the DiffServ technique has became the most promising so- lution, mainly for its simplicity with respect to different alternatives. Moreover, in satellite communication systems, DiffServ policy computational capabilities are placed at the edge points (end-to-end philosophy); this is very important for a network constituted by one satellite link because it allows to reduce the implementation complexity of the satellite on-board equipments. The satellite switch under consideration makes use of the Multiple Input Queuing approach. Packets arrived at a switch input are stored in a shared buffer but they are logically ordered in individual queues, one for each possible output link. According to the DiffServ policy, within a same logical queue, packets are reordered in individual sub-queues according to the priority. A suitable implementation of the DiffServ policy based on a Cellular Neural Network (CNN) is proposed in the paper in order to achieve QoS requirements. The CNNs are a set of linear and nonlinear circuits connected among them that allow parallel and asynchronous computation. CNNs are a class of neural networks similar to Hopfield Neural Networks (HNN), but more flexible and suitable for solving the output contention problem, inherent of switching systems, for VLSI implementation. In this paper a CNN has been designed in order to maximize a cost functional, related to the on-board switch through- put and QoS constraints. The initial state for each neural cell is obtained looking at the presence of at least one packet from a certain input logical queue to a specific output line. The input value for each neural cell is a function of priority and length of each input logical queue. The versatility of neural network make feasible to take the best decision for the packet to be delivered to each output satellite beam, in order to meet specific QoS constraints. Numerical results for CNN approach highlights that Neural network convergence within a time slot is guaranteed, and an optimal, or at least near-optimal, solution in terms of cost function is achieved.

A Cellular Neural Networks Based DiffServ Switch for Satellite Communication Systems / R. Fantacci;R. Gubellini;T. Pecorella;D. Tarchi. - STAMPA. - (2003), pp. 1-10. (Intervento presentato al convegno ASMS 2003 tenutosi a Frascati, RM, USA).

A Cellular Neural Networks Based DiffServ Switch for Satellite Communication Systems

FANTACCI, ROMANO;PECORELLA, TOMMASO;TARCHI, DANIELE
2003

Abstract

Recent developments of Internet services and advanced compression methods has revived interest on IP based multimedia satellite communication systems. However a main problem arising here is to guarantee specific Quality of Service (QoS) constraints in order to have good performance for each traffic class. Among various QoS approach used in Internet, recently the DiffServ technique has became the most promising so- lution, mainly for its simplicity with respect to different alternatives. Moreover, in satellite communication systems, DiffServ policy computational capabilities are placed at the edge points (end-to-end philosophy); this is very important for a network constituted by one satellite link because it allows to reduce the implementation complexity of the satellite on-board equipments. The satellite switch under consideration makes use of the Multiple Input Queuing approach. Packets arrived at a switch input are stored in a shared buffer but they are logically ordered in individual queues, one for each possible output link. According to the DiffServ policy, within a same logical queue, packets are reordered in individual sub-queues according to the priority. A suitable implementation of the DiffServ policy based on a Cellular Neural Network (CNN) is proposed in the paper in order to achieve QoS requirements. The CNNs are a set of linear and nonlinear circuits connected among them that allow parallel and asynchronous computation. CNNs are a class of neural networks similar to Hopfield Neural Networks (HNN), but more flexible and suitable for solving the output contention problem, inherent of switching systems, for VLSI implementation. In this paper a CNN has been designed in order to maximize a cost functional, related to the on-board switch through- put and QoS constraints. The initial state for each neural cell is obtained looking at the presence of at least one packet from a certain input logical queue to a specific output line. The input value for each neural cell is a function of priority and length of each input logical queue. The versatility of neural network make feasible to take the best decision for the packet to be delivered to each output satellite beam, in order to meet specific QoS constraints. Numerical results for CNN approach highlights that Neural network convergence within a time slot is guaranteed, and an optimal, or at least near-optimal, solution in terms of cost function is achieved.
2003
Proceedings of the First International Conference on Advanced Satellite Mobile Systems ASMS 2003
ASMS 2003
Frascati, RM, USA
R. Fantacci;R. Gubellini;T. Pecorella;D. Tarchi
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/384407
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