A QoS Provisioning Recurrent Neural Network based Call Admission Control for beyond 3G Networks
The Call admission control (CAC) is one of the Radio Resource
Management (RRM) techniques that plays influential role in
ensuring the desired Quality of Service (QoS) to the users and
applications in next generation networks. This paper proposes a
fuzzy neural approach for making the call admission control
decision in multi class traffic based Next Generation Wireless
Networks (NGWN). The proposed Fuzzy Neural call admission
control (FNCAC) scheme is an integrated CAC module that
combines the linguistic control capabilities of the fuzzy logic
controller and the learning capabilities of the neural networks.
The model is based on recurrent radial basis function networks
which have better learning and adaptability that can be used to
develop intelligent system to handle the incoming traffic in an
heterogeneous network environment. The simulation results are
optimistic and indicates that the proposed FNCAC algorithm
performs better than the other two methods and the call blocking
probability is minimal when compared to other two methods.
Keywords: Radio resource management, Heterogeneous wireless
Networks, Call admission control, Call blocking probability,
Recurrent radial basis function networks
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