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A Neural Network Approach to Cell Loss Rate Estimation for Call Admission Control in ATM Networks
IEICE TRANSACTIONS on Communications
Publication Date: 1997/03/25
Print ISSN: 0916-8516
Type of Manuscript: PAPER
Category: Communication Networks and Services
ATM, CAC, cell loss rate, neural network, Kalman filter,
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The asynchronous transfer mode (ATM) provides efficient switching capability for various kinds of communication services. To guarantee the minimum quality of services in the ATM networks, the bandwidth allocation setup procedure between the network nodes and users is very important. However, most of call admission control (CAC) methods which have been proposed so far are not fully appropriate to apply to real environments in terms of the complexity of the hardware implementation or the accuracy of assumptions about the cell-arrival processes. In addition, the success of broad bandwidth applications in the future multimedia environments will largely depend on the degree to which the efficiency in communication systems can be achieved, so that establishing high-speed CAC schemes in the ATM networks is an indispensable subject. This paper proposes a new cell-loss rate estimation method for the real time CAC in ATM networks. A neural network model using the Kalman filter algorithm was employed to improve the error minimizing process for the cell-loss estimation problem. In the process of optimizing the three-layer perceptron, the average, the variance, and the 3rd central moment of the number of cell arrivals were calculated, and cell-loss rate date based on the non-parametric method were adopted for outputs of the neural network. Evaluation results concerned with the convergence using the sum of square errors of outputs were also discussed in this paper. Using this algorithm, ATM cell-loss rates can be easily derived from the average and peak of cells rates coming from users. Results for the cell-loss estimation process suggest that the proposed method will be useful for high-speed ATM CAC in multimedia traffic environments.