• Journal of the Institute of Convergence Signal Processing
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• v.1 no.2
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• pp.97-104
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• 2000

Effective call admission control is desirable to control an ATM traffics. It should provide high fairness and utilization for different kinds of services during call admission. Complete bandwidth sharing method is efficient for utilization of bandwidth but not efficient for fairness of call admission. Complete bandwidth partitioning method is efficient for fairness but not efficient for utilization. We propose a new CST(Complete Sharing with Threshold) algorithm using threshold on a total link to improve fairness and utilization.

• The Transactions of the Korea Information Processing Society
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• v.4 no.8
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• pp.2070-2079
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• 1997

In this paper, we proposed the FNCAC (fuzzy neural call admission control) scheme of the ATM networks which used the benefits of fuzzy logic controller and the learning abilities of the neural network to solve the call admission control problems. The new call in ATM networks is connected if QoS(quality of service) of the current calls is not affected due to the connection of a new call. The neural network CAC(call admission control) system is predictable system because the neural network is able to learn by the input/output pattern. We applied the fuzzy inference on the learning rate and momentum constant for improving the learning speed of the fuzzy neural network. The excellence of the proposed algorithm was verified using measurement of learning numbers in the traditional neural network method and fuzzy neural network method by simulation. We found that the learning speed of the FNCAC based on the fuzzy learning rules is 5 times faster than that of the CAC method based on the traditional neural network theory.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.2897-2908
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• 1996

This paper presents an effective call admission control algorithm using the Common Pool on the virtual path in ATM networks. Call admission control decides whether or not to accept a new call, so as to ensure the service quality of both individiual existing calls and of the new call itself. In the proposed algorithm, a new call is accepted when the sum of the bandwidths of existing calls and of the new call will not exceed lind capacity. If the sum of their bandwidths exceed link capacity, reserved bandwidth of Common Pool is considreed to accept the new call. Computer simulation results using a simuple network model are algorithm given to evaluate accuracy and call blocking probability by the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2184-2196
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• 1998

Admission control is one of the most important congestion control mechanism to be executed at the call set up phase by regulating traffic into a network in a preventive way. An efficient QOS evaluation or bandwidth estimation method is required for call admission to be decided in real time. In this paper, we spropose a computtionally simple approximation method of estimating cell loss probability and mean cell delay for admission control of both delay sensitive and loss sensitive calls. Mixed input queueing system, where a new call combines with the existing traffic, is used as a queueing model for QOS estimation. Also traffic parameters are suggested to characterize both a new call and existing traffic. Aggregate traffic is approximated by a renewal process with these traffic parameters and then mean delay and cell loss probability are detemined using appropriate approximation formulas. The accuracy of this approximation approach is examined by comparing their results with exact analysis or simulation results of vrious mixed unput queueing systems. Based on this QOS estimation method, call admission control scheme which is traffic independent and computable in yeal time are proposed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.1958-1969
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• 1996

This paper proposed a new approach to adaptive call admission control based on a neural network for multiple service classes with different quality of service (QoS) in the ATM-based Broadband Integrated Services Digital Networks. the proposed method extend Hiramatsu's neural network based "leaky pattern table" method for the single QoS[1, 2, 3] to deal with multiple services with different QoS by constructing multiple pattern tables based on each service's acceptance or rejection at the call set-up requests, and by simultaneously controlling each service's QoS according to the target QoS of the service and the trunk capacity. Computer simulation results on two service classes with different traffic characteristics and different cell loss rates as QoS, highlight good performance and effectiveness of the proposed call admission controller for multiple service classes.e classes.

• Proceedings of the IEEK Conference
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• 2000.06a
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• pp.115-118
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• 2000

In order to control the flow of traffics in ATM networks and optimize the usage of network resources, an efficient control mechanism is necessary to cope with congestion and prevent the degradation of network performance caused by congestion. To effectively control traffic in UNI(User Network Interface) stage, we proposed algorithm of integrated model using on-line teaming neural network for CAC(Call Admission Control) and UPC(Usage Parameter Control). Simulation results will show that the proposed adaptive algorithm uses of network resources efficiently and satisfies QoS for the various kinds of traffics.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.3
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• pp.1-8
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• 1998

In this paper, we propose a Neural Call Admission Control (CAC) method using a Sparse Distributed Memory(SDM). CAC is a key technology of TM network traffic control. It should be adaptable to the rapid and various changes of the ATM network environment. conventional approach to the ATM CAC requires network analysis in all cases. So, the optimal implementation is said to be very difficult. Therefore, neural approach have recently been employed. However, it does not mett the adaptability requirements. because it requires additional learning data tables and learning phase during CAC operation. We have proposed a neural network CAC method based on SDM that is more actural than conventioal approach to apply it to CAC. We compared it with previous neural network CAC method. It provides CAC with good adaptability to manage changes. Experimenatal results show that it has rapid adaptability and stability without additional learning table or learning phase.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2188-2195
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• 1999

This paper proposes a new call admission control scheme utilizing an inverse fuzzy vector quantizer(IFVQ) and neuralnet, which combines benefits of IFVQ and flexibilities of FCM(Fuzzy-C-Means) arithmetics, to decide whether a requested call not to be trained in learning phase to be connected or not. The system generates the estimated traffic pattern for the cell stream of a new call, using feasible/infeasible patterns in codebook, fuzzy membership values that represent the degree to which each pattern of codebook matches input pattern, and FCM arithmetics. The input to the NN is the vector consisted of traffic parameters which are the means and variances of the number of cells arriving in decision as to whether to accept or reject a new call depends on whether the NN is used for decision threshold(+0.5). This method is a new technique for call admission control using the membership values as traffic parameter which declared to CAC at the call set up stage, and this is valid for a very general traffic model in which the calls of a stream can belong to an unlimited number of traffic classes. Through the simulations, it is founded the performance of the suggested method outperforms compared to the conventional NN method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.173-179
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• 1998

This paper proposes a new call admission control scheme utilizing an inverse fuzzy vector quantizer(IFVQ) and neural net, which combines benefits of IFVQ and flexibilities of FCM(Fuzzy-C-Menas) arithmatics, to decide whether a requested call that is not trained in learning phase to be connected or not. The system generates the estimated traffic pattern of the cell stream of a new call, using feasible/infeasible patterns in codebook, fuzzy membership values that represent the degree to which each pattern of codebook matches input pattern, and FCM arithmatics. The input to the NN is the vector consisted of traffic parameters which is the means and variances of the number of cells arriving inthe interval. After training(using error back propagation algorithm), when the NN is used for decision making, the decision as to whether to accept or reject a new call depends on whether the output is greater or less then decision threshold(+0.5). This method is a new technique for call admi sion control using the membership values as traffic parameter which declared to CAC at the call set up stage, and is valid for a very general traffic model in which the calls of a stream can belong to an unlimited number of traffic classes. Through the simmulation. it is founded the performance of the suggested method outforms compared to the conventional NN method.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.94-97
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• 1999

In this paper, a new real-time neural network connection admission controller is proposed. The proposed controller measures traffic flows, cell loss rate and cell delay periodically each classes. The Neural network learns the relation between those measured information and service quality by real-time. Also the proposed controller uses the DWRR multiplexer with buffer dedicated to every traffic source in order to measure the delay that cells experience in buffer. Experimental result shows that the proposed method can control effectively heterogeneous traffic sources with diverse QoS requirement.