An Efficient Framework Using Fuzzy Logic and Multi Agent System for Cellular Network

Kamble, Megha; Gupta, Rohit

doi:10.1007/978-81-322-0487-9_50

Cited by 1 publication

(2 citation statements)

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“…The diagrammatic representation of the proposed work is shown in the Figure 1. The layered architecture adapted for Multi Agent system and functional architecture is elaborated in [10] [11]. The following section discusses about the layers.…”

Section: Proposed Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Software Agent Structure for Performance Index Improvement of Cellular Network

Kamble¹,

Gupta²

2014

IJCNS

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Efficient reuse of limited radio spectrum is vital issue to support increasing number of mobile terminals and heterogeneous traffic scenarios. Dynamic channel allocation (DCA) technique is suitable to solve the problem. The drawback of dynamic channel allocation is it may upgrade performance of one cluster and degrade performance of other cluster in large scale cellular network. To balance performance of clusters and increase carried traffic in network, there is need of enhancement of DCA techniques. To introduce improvement in the dynamic channel approach, the paper suggested Multi Agent System (MAS) of physical agents ported at base stations working on the principle of cooperative negotiation to improve the QoS of the network. We formulated an integrated framework which includes fundamental mechanism of call admission control and resource management using hybrid channel allocation (HCA). To balance performance index of various clusters of network, agent negotiation is executed. Our simulation results show that it is possible to significantly enhance performance index of network due to MAS-HCA approach when compared with ES based and ILP based HCA schemes proposed in literature.

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Section: Proposed Methodologymentioning

confidence: 99%

“…More details of fuzzy logic implementation at reactive layer are presented in [10] and experimental results are included in Section 3 in this paper. Novelty of the work lies in redesigning the fuzzy logic parameters [11] and using defuzzification formulae for call buffering delay parameter.…”

Section: Reactive Layer (Rl)mentioning

confidence: 99%