1 research outputs found
An Intelligent Call Admission Control Decision Mechanism for Wireless Networks
The Call admission control (CAC) is one of the Radio Resource Management
(RRM) techniques plays instrumental role in ensuring the desired Quality of
Service (QoS) to the users working on different applications which have
diversified nature of QoS requirements. This paper proposes a fuzzy neural
approach for call admission control in a 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 (RRBFN) which have better learning and adaptability
that can be used to develop the intelligent system to handle the incoming
traffic in the heterogeneous network environment. The proposed FNCAC can
achieve reduced call blocking probability keeping the resource utilisation at
an optimal level. In the proposed algorithm we have considered three classes of
traffic having different QoS requirements. We have considered the heterogeneous
network environment which can effectively handle this traffic. The traffic
classes taken for the study are Conversational traffic, Interactive traffic and
back ground traffic which are with varied QoS parameters. The paper also
presents the analytical model for the CAC .The paper compares the call blocking
probabilities for all the three types of traffic in both the models. The
simulation results indicate that compared to Fuzzy logic based CAC,
Conventional CAC, The simulation results are optimistic and indicates that the
proposed FNCAC algorithm performs better where the call blocking probability is
minimal when compared to other two methods.Comment: Journal of Computing online at
https://sites.google.com/site/journalofcomputing