TY - GEN
T1 - Neural Network controller for rearrangeable switching networks
AU - Park, Young Keun
AU - Cherkassky, Vladimir
PY - 1993/1/1
Y1 - 1993/1/1
N2 - The rapid evolution in the field of communication networks requires high speed switching technologies. This involves a high degree of parallelism in switching control and routing performed at the hardware level. In this paper a neural network approach to controlling a three stage Clos network in real time is proposed. This controller provides optimal routing of communication traffic requests on a call-by-call basis by rearranging existing connections with a minimum length of rearrangement sequence so that a new blocked call request can be accommodated. The proposed neural network controller uses Paull's rearrangement algorithm, along with the special (least used) switch selection rule in order to minimize the length of rearrangement sequences. The functional behavior of our model is verified by simulations and it is shown that the convergence time required for finding an optimal solution is constant regardless of the switching network size. The performance is evaluated for random traffic with various traffic loads. Simulation results show that applying the least used switch selection rule increases the efficiency in switch rearrangements, reduces the network convergence time and also keeps the network from being trapped in local minima. The implementation aspects are also discussed to show the feasibility of the proposed approach.
AB - The rapid evolution in the field of communication networks requires high speed switching technologies. This involves a high degree of parallelism in switching control and routing performed at the hardware level. In this paper a neural network approach to controlling a three stage Clos network in real time is proposed. This controller provides optimal routing of communication traffic requests on a call-by-call basis by rearranging existing connections with a minimum length of rearrangement sequence so that a new blocked call request can be accommodated. The proposed neural network controller uses Paull's rearrangement algorithm, along with the special (least used) switch selection rule in order to minimize the length of rearrangement sequences. The functional behavior of our model is verified by simulations and it is shown that the convergence time required for finding an optimal solution is constant regardless of the switching network size. The performance is evaluated for random traffic with various traffic loads. Simulation results show that applying the least used switch selection rule increases the efficiency in switch rearrangements, reduces the network convergence time and also keeps the network from being trapped in local minima. The implementation aspects are also discussed to show the feasibility of the proposed approach.
UR - http://www.scopus.com/inward/record.url?scp=84943238354&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84943238354&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84943238354
SN - 0780312007
T3 - 1993 IEEE International Conference on Neural Networks
SP - 1896
EP - 1901
BT - 1993 IEEE International Conference on Neural Networks
PB - Publ by IEEE
T2 - 1993 IEEE International Conference on Neural Networks
Y2 - 28 March 1993 through 1 April 1993
ER -