Introduction to artificial neural systems
Introduction to artificial neural systems
SIGCOMM '93 Conference proceedings on Communications architectures, protocols and applications
Asynchronous transfer mode (2nd ed.): solution for broadband ISDN
Asynchronous transfer mode (2nd ed.): solution for broadband ISDN
Teletraffic Technologies in ATM Networks
Teletraffic Technologies in ATM Networks
Queueing Theory for Computer Communications
Queueing Theory for Computer Communications
Neural Networks in Telecommunications
Neural Networks in Telecommunications
Intelligent traffic control for ATM broadband networks
IEEE Communications Magazine
Modeling and performance comparison of policing mechanisms for ATM networks
IEEE Journal on Selected Areas in Communications
Analysis of interdeparture processes for bursty traffic in ATM networks
IEEE Journal on Selected Areas in Communications
Dynamic call admission control in ATM networks
IEEE Journal on Selected Areas in Communications
A call admission control scheme for ATM networks using a simple quality estimate
IEEE Journal on Selected Areas in Communications
Resource allocation for broadband networks
IEEE Journal on Selected Areas in Communications
ATM communications network control by neural networks
IEEE Transactions on Neural Networks
Neural network control of communications systems
IEEE Transactions on Neural Networks
Access control of parallel multiserver loss queues
Performance Evaluation
Hi-index | 0.24 |
Call Admission Control (CAC) has been accepted as a potential solution for supporting diverse, heterogeneous traffic sources demanding different Quality of Services (QOSs) in ATM networks. Also, CAC is required to consume a minimum of time and space to make call acceptance decisions. In this paper, we present an efficient neutral-network-based CAC (NNCAC) mechanism for ATM networks with heterogeneous arrivals. All heterogeneous traffic calls are initially categorized into various classes. Based on the number of calls in each class, NNCAC efficiently and accurately estimates the cell delay and cell loss ratio of each class in real time by means of a pre-trained neutral network. According to our decent study which exhibits the superiority of the employment of analysis-based training data over simulation-based data, we particularly construct the training data from a heterogeneous-arrival dual-class queueing model M^[^N^"^1^] + I^[^N^"^2^]/D/1/K, where M and I represent the Bernoulli and interrupted Bernoulli processes, and N"1 and N"2 represent the corresponding numbers of calls, respectively. Analytic results of the queueing model are confirmed by simulation results. Finally, we demonstrate the profound agreement of our neural-network-based estimated results with analytic results, justifying the viability of our NNCAC mechanism.