A. A. Tarraf, I. W. Habib, and T. N. Saadawi. Intelligent traffic control for ATM broadband networks. IEEE Communications Magazine, 33(10):76--82, October 195.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....42 869 5431, e mail: shoh(no. non.kaist.ac3a) Soo Young Lee (phone: 82 42 869 3431, e mail: sylee4eelatist.ac3a) I. INTRODUCTION Multilayer perceptron (MLP) is the most popular neural network model which has wide application areas such as mobile telecommunications [1] 2] ATM networks [3] [4], pattern recognition [5] speech recognition [6] time series prediction [7] and nonlinear control [8] Especially theoretical analyses of MLPs in mathematical or statistical aspects support the applications and research efforts for MLPs [9] 12] Training of MLPs is usually done by the error ....

I. W. Habib, A. A. Tarrag and T. N. Saadawi, "Intelligent Traffic Control for ATM Broadband Networks," IEEE Communications Magazine, Vol. 33, 1995, pp. 76-85.

....of offered service rate are maintained at higher levels by minimizing the cell losses and delays due to congestion. Following paragraphs summarize the research utilizing CI techniques for implementation of congestion and rate control algorithms in ATM networks. Tarraf, Habib and Saadawi [TH94,THS95,THS95b] have investigated extensively how ANNs can be used to solve many of the problems encountered in the development of coherent traffic control strategies in ATM networks. In [THS95b] they present congestion control schemes for ATM networks. Also, they investigate a reinforcement learning ....

....offered service rate are maintained at higher levels by minimizing the cell losses and delays due to congestion. Following paragraphs summarize the research utilizing CI techniques for implementation of congestion and rate control algorithms in ATM networks. Tarraf, Habib and Saadawi [TH94,THS95,THS95b] have investigated extensively how ANNs can be used to solve many of the problems encountered in the development of coherent traffic control strategies in ATM networks. In [THS95b] they present congestion control schemes for ATM networks. Also, they investigate a reinforcement learning based ....

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A. A. Tarraf, I. W. Habib, and T. N. Saadawi. Intelligent traffic control for ATM broadband networks. IEEE Communications Magazine, 33(10):76--82, October 195.

....traffic sources and network behaviour is difficult, especially if they need to be done in real time. Neural network approaches which do not require precise models of the network processes have been used with varying degrees of success for traffic management and congestion control [8] 9] 11] [10]. II. CONNECTION ADMISSION CONTROL (CAC) When a user wishes to establish a connection with another party, his terminal sends a connection set up request to the Connection Admission Control (CAC) controller, during which it declares information such as the required QoS and its own traffic ....

A.A. Tarraf, I.W. Habib, and T.N. Saadawi. Intelligent Traffic Control for ATM Broadband Networks. IEEE Communications Magazine, pp. 76-82, Oct. 1995.

....rate are maintained at higher levels by minimizing the cell losses and delays due to congestion. Following sections summarize the research utilizing CI techniques for implementation of congestion and rate control algorithms in ATM networks. Tarraf, Habib and Saadawi [Tarraf and Habib, 1994, Tarraf et al. 195, Tarraf et al. 1995b] have investigated extensively how ANNs can be used to solve many of the problems encountered in the development of coherent traffic control strategies in ATM networks. In [Tarraf et al. 1995b] they present congestion control schemes for ATM networks. Also, they ....

Tarraf, A. A., Habib, I. W., and Saadawi, T. N. (195). Intelligent traffic control for ATM broadband networks. IEEE Communications Magazine, 33(10):76--82.

....(Lippmann [15] and implement nonlinear mappings. A review of training algorithms have been presented by Hiramatsu [14] and applications in communications have been discussed by Posner [18] Neural networks are specially suitable for prediction (Neves [17] and control (Necker [16] and Tarraf [19]) Frequency domain techniques like spectral analysis has also been applied to model wide band input processes in ATM networks (Alqaed and Chang [41] In addition, wavelet coding has also been explored. Wavelets provide a convenient way to describe signals in the time frequency domain (Schiff ....

A. Tarraf, I. Habib, and T. Saadawi, "Intelligent traffic control for ATM broadband networks", IEEE Commun. Mag., pp. 76-82, October 1995. Self-similar Traffic Models

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A. A. Tarraf, I. W. Habib, and T. N. Saadawi. Intelligent traffic control for ATM broadband networks. IEEE Communications Magazine, 33(10):76--82, October 195.

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A. A. Tarraf, I. W. Habib, and T. N. Saadawi, "Intelligent traffic control for ATM broadband networks," IEEE Communications Magazine 33, pp. 76--82, Oct. 195.

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Tarraf, A, Habib, I and Saadawi, T "Intelligent Traffic Control for ATM Broadband Networks", IEEE

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A.A. Tarraf, I.W. Habib, and T.N. Saadawi. Intelligent traffic control for ATM broadband networks. IEEE Communications Magazine, pp. 76-82, Oct. 1992.

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