Software Agent Structure for Performance Index Improvement of Cellular Network

Megha Kamble; Roopam Gupta

doi:10.4236/ijcns.2014.79035

Home > Journals > Computer Science & Communications > IJCNS

International Journal of Communications,... > Vol.7 No.9, September 2014

Software Agent Structure for Performance Index Improvement of Cellular Network ()

Megha Kamble, Roopam Gupta

Department of Information Technology, University Institute of Technology, R.G.P.V., Bhopal, India.

DOI: 10.4236/ijcns.2014.79035 PDF HTML 2,683 Downloads 3,126 Views Citations

Abstract

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.

Keywords

Cellular Network, Hybrid Channel Allocation, Multi Agent System, Agent Negotiation

Share and Cite:

Kamble, M. and Gupta, R. (2014) Software Agent Structure for Performance Index Improvement of Cellular Network. International Journal of Communications, Network and System Sciences, 7, 331-345. doi: 10.4236/ijcns.2014.79035.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Papazoglou, P.M. and Karras, D.A. (2008) An Improved Multi-Agent Simulation Methodology For Modelling and Evaluating Wireless Communication Systems Resource Allocation Algorithms. Journal Of Universal Computer Science, 14, 1061-1079.
[2]	Papazoglou, P.M., Karras, D.A. and Papademetriou, R.C. (2011) On Multi Agent Based Modeling and Control in Large Scale Wireless Communication Systems for Improved Resource Allocation Performance. Telecommunication System, 52, 1657-1675. http://dx.doi.org/10.1007/s11235-011-9658-2
[3]	Lekova, A. (2012) Exploiting Mobile Ad Hoc Networking and knowledge generation to Achieve Ambient Intelligence. Applied Computational Intelligence and Soft Computing, 2012, Article ID: 262936.
[4]	Elhachmi, J. and Guenoun, Z. (2011) Distributed Frequency Assignement Using Hierarchical Cooperative Multi-Agent System. International Journal of Communications, Network and System Sciences, 4, 727-734.
[5]	Wu, X., Kaekel, A., Bari, A. and Ngom, A. (2010) Optimized Hybrid Resource Allocation in Wireless Cellular Networks with and without Channel Reassignment. Hindawi Publishing Corporation. Journal of Computing Systems, Networks and Communications, 2010, Article ID: 524854.
[6]	Elhachimi, J. and Guennoun, Z. (2011) A Multi-Agent System for Resource Management in GSM Cellular Networks. In: Abraham, A., et al., Eds., International Symposium on DCAI, AISC 91, 99-106.
[7]	Bodanese, E.L. and Cuthbert, L.G. (2000) Application of Intelligent Agents in Channel Allocation Strategies for Mobile Networks. IEEE International Conference on Communications, 1, 18-22.
[8]	Iraqi, Y. and Boutaba, R. (2000) A Multi-Agent System for Resource Management in Wireless Mobile Multimedia Networks. In: Ambler, A., Calo, S.B. and Kar G., Eds., DSOM 2000, LNCS 1960, 218-229 Springer-Verlag, Berlin Heidelberg.
[9]	Bigham, J. and Du, L. (2003) Cooperative Negotiation in a Multi-Agent System for Real-Time Load Balancing of a Mobile Cellular Network. AAMAS’03, Melbourne, 14-18 July 2003.
[10]	Kamble, M. and Gupta, R. (2011) Multi Agent System Architecture for Admission Control and Resource Allocation in Cellular Network. Communications in Computer and Information Science, 250, 576-578. http://dx.doi.org/10.1007/978-3-642-25734-6_99
[11]	Kamble, M. and Gupta, R. (2011) An Efficient Framework Using Fuzzy Logic and Multi Agent System for Cellular Network. In: Deep, K., Nagar, A., Pant, M. and Bansal, J.C., Eds., Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011, Springer, India, 513-523.
[12]	Jain, P.J., et al. (2002) On Distributed Dynamic Channel Allocation in Mobile Cellular Networks. IEEE Transactions on Parallel and Distributed Systems, 13, 1024-1037.
[13]	Vidyarthi, G., Ngom, A. and Stojmenovic, I. (2005) A Hybrid Channel Assignment Approach Using an Efficient Evolutionary Strategy in Wireless Network. IEEE Transactions on Vehicular Technology, 54, 1887-1895.
[14]	Kamble, M. and Gupta R. (2011) A New Framework for Call Admission Control in Wireless Cellular Network. 2011 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), Udaipur, 22-24 April 2011, 178-181.
[15]	Pendharkar, P.C. (2006) A Multi-Agent Distributed Channel Allocation Approach For Wireless Networks. International Transactions in Operation Research, 15, 325-337.
[16]	Lee, W.C.Y. (1989) Mobile Cellular Telecommunication Systems. McGraw-Hill, New York.
[17]	http://jade.tilab.com/