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The Call admission control (CAC) is one of the Radio Resource Management(RRM) techniques that plays influential role in ensuring the desired Quality ofService (QoS) to the users and applications in next generation networks. Thispaper proposes a fuzzy neural approach for making the call admission controldecision in multi class traffic based Next Generation Wireless Networks (NGWN).The proposed Fuzzy Neural call admission control (FNCAC) scheme is anintegrated CAC module that combines the linguistic control capabilities of thefuzzy logic controller and the learning capabilities of the neural networks.The model is based on recurrent radial basis function networks which havebetter learning and adaptability that can be used to develop intelligent systemto handle the incoming traffic in an heterogeneous network environment. Thesimulation results are optimistic and indicates that the proposed FNCACalgorithm performs better than the other two methods and the call blockingprobability is minimal when compared to other two methods.

A QoS Provisioning Recurrent Neural Network based Call Admission Control for beyond 3G Networks