Multilayer feedforward networks are universal approximators
Neural Networks
Approximation capabilities of multilayer feedforward networks
Neural Networks
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
A QoS-Provisioning neural fuzzy connection admission controller for multimedia high-speed networks
IEEE/ACM Transactions on Networking (TON)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
An Introduction to Neural Networks
An Introduction to Neural Networks
Applications of neural networks in high-speed communication networks
IEEE Communications Magazine
Call admission control and routing in integrated services networks using neuro-dynamic programming
IEEE Journal on Selected Areas in Communications
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Wireless Local Area Networks (WLANs) are particularly difficult to manage due to the highly dynamic nature of the traffic, caused by variations on the number of users, their locations and the type applications they use. In this paper, we propose a new modeling approach, based on neural networks, that is able to predict the Quality of Service (QoS) of WLANs based on the characterization of an operational scenario. From measurements of the inbound and outbound traffic at each Access Point (AP) and of the QoS perceived at each Cell, the model estimates the QoS when the number of users grows. The model does not require the knowledge of the exact network characteristics, since it is only based on measurements carried out at the APs. This modeling approach can be of great help in the planning and management of WLANs. Several realistic network scenarios were defined in order to test the validity of the model. The results show that the model can achieve excellent performance, since the QoS prediction is accurate even when there are significant changes in the number of users.