An Artificial Neural Network Approach for Mechanisms of Call Admission Control in UMTS 3G Networks

Ribeiro, A.I.; Duarte-Figueiredo, Fátima de L. P.; Novy, G.; Storck, Carlos Renato; Dias, Sérgio M.; Zárate, Luis Enrique

doi:10.1109/his.2008.39

Cited by 5 publications

(5 citation statements)

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“…Thus, Neural CAC-RD (CAC-RDNN) [2] representation is a non-linear mapping between several inputs and outputs parameters (see Table I To obtain the neural representation, a synthetic database was used. Simulation results using CAC-RD [1] were utilized to compose the synthetic database.…”

Section: Neural Cac-rdmentioning

confidence: 99%

“…To obtain a representative set of the process like the training set, a statistical technique from [11] was used to build a better definition of the data training set. How it was applied it is described in [2].…”

Section: A Collecting Datamentioning

confidence: 99%

“…To determine how many samples are needed in each region, we presented Samples Generator (SG) algorithm [2]. Equation (1) creates a parabolic function for each region.…”

Section: A Collecting Datamentioning

confidence: 99%

“…Computational and execution time limits were found. To solve this problem in [2], a Neural CAC-RD (CAC-RDNN) was presented. Simulation results shown that CAC-RDNN can treat the admission control to big networks, solving the problem to apply CAC-RD in real 3G environments.…”

Section: B Cac-rdnn New Trainingmentioning

confidence: 99%

“…Due to time and memory computational resources limits, it was impossible to predict the behavior of a CAC in a real network. As in the last years Artificial Neural Networks (ANN) are being proposed as powerful computational tools due to the low time of processing (network in operation), we represented CAC-RD through a neural model to solve the limitation of users and antennas for the simulations [2]. The neural model was used in the implementation of this control, generating the Neural CAC-RD (CAC-RDNN).…”

Section: Introductionmentioning

confidence: 99%

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A representative validation of a neural 3G admission control through rules extraction

Ribeiro

Zárate

Figueiredo

2009

2009 International Joint Conference on Neural Networks

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The proposal of new mechanisms and systems of call admission control has as objective the ensuring quality of service (QoS) for mobile networks. These mechanisms are evaluated through simulators, presenting some computational limitations. Recently, CAC-RD (Call Admission Control based on Reservation and Diagnosis) has been proposed as a new mechanism to simulate 3G UMTS networks, but a computational resources limitation was found. As example, to simulate 600 s of traffic with a limit of 1100 users, the simulation spent approximately 19 hours. This prevents a more real simulation in the presence of greater number of users. Recently, to surmount these limitations, the authors have presented CAC-RDNN (CAC-RD Neural Network), a new approach based on neural networks to represent quantitatively the CAC-RD, but it was verified that the new representation fails when qualitative aspects are evaluated. In this paper, the qualitative analysis of the mathematical representation of CAC-RDNN is evaluated. Results showed that CAC-RDNN reflects CAC-RD modules behavior in both quantitative and qualitative aspects, being able to simulate real networks.

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Section: Neural Cac-rdmentioning

confidence: 99%

Section: A Collecting Datamentioning

confidence: 99%

“…To determine how many samples are needed in each region, we presented Samples Generator (SG) algorithm [2]. Equation (1) creates a parabolic function for each region.…”

Section: A Collecting Datamentioning

confidence: 99%

Section: B Cac-rdnn New Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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