Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A unified approach to bandwidth allocation and access control in fast packet-switched networks
IEEE INFOCOM '92 Proceedings of the eleventh annual joint conference of the IEEE computer and communications societies on One world through communications (Vol. 1)
Queue response to input correlation functions: discrete spectral analysis
IEEE/ACM Transactions on Networking (TON)
Queue response to input correlation functions: continuous spectral analysis
IEEE/ACM Transactions on Networking (TON)
ATM networks (2nd ed.): concepts, protocols, applications
ATM networks (2nd ed.): concepts, protocols, applications
Design of a fuzzy traffic controller for ATM networks
IEEE/ACM Transactions on Networking (TON)
A neural-net based fuzzy admission controller for an ATM network
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 2
Predictive dynamic bandwidth allocation for efficient transport of real-time VBR video over ATM
IEEE Journal on Selected Areas in Communications
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
ATM (asynchronous transfer mode) systems can support services with bursty traffic. An ATM system needs a sophisticated and real-time connection admission controller not only to guarantee the required quality-of-service (QoS) for existing calls but also to raise the system efficiency. Input process has a power-spectral-density (PSD) which explicitly contains the correlation behavior of input traffic and has a great impact on the system performance. Also, a neural network has been widely applied to deal with traffic control related problems in ATM systems because of its self-learning capability. In this paper, we propose a PSD-based neural-net connection admission control (PNCAC) method for an ATM system. Under the QoS constraint, we construct a decision hyperplane of the connection admission control according to parameters of the power spectrum. We further adopt the learning/adapting capabilities of the neural network to adjust the optimum location of the boundary between these two decision spaces. Simulation results show that the PNCAC method provides a superior system utilization over the conventional CAC schemes by an amount of 18%, while keeping the QoS contract.