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Cell Grouping Design for Wireless Network using Artificial Bee Colony

인공벌군집을 적용한 무선네트워크 셀 그룹핑 설계

  • Received : 2016.01.25
  • Accepted : 2016.05.03
  • Published : 2016.06.30

Abstract

In mobile communication systems, location management deals with the location determination of users in a network. One of the strategies used in location management is to partition the network into location areas. Each location area consists of a group of cells. The goal of location management is to partition the network into a number of location areas such that the total paging cost and handoff (or update) cost is a minimum. Finding the optimal number of location areas and the corresponding configuration of the partitioned network is a difficult combinatorial optimization problem. This cell grouping problem is to find a compromise between the location update and paging operations such that the cost of mobile terminal location tracking is a minimum in location area wireless network. In fact, this is shown to be an NP-complete problem in an earlier study. In this paper, artificial bee colony (ABC) is developed and proposed to obtain the best/optimal group of cells for location area planning for location management system. The performance of the artificial bee colony (ABC) is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters. The important control parameter of ABC is only 'Limit' which is the number of trials after which a food source is assumed to be abandoned. Simulation results for 16, 36, and 64 cell grouping problems in wireless network show that the performance of our ABC is better than those alternatives such as ant colony optimization (ACO) and particle swarm optimization (PSO).

Acknowledgement

Supported by : Kangwon National University, Institute for Information and communications Technology Promotion (IITP)

References

  1. Baek, D.H. and Jin, H.C., A Feasibility Study on the Location Based Services under Ubiquitous Environment, Journal of the Society of Korea Industrial and Systems Engineering, 2007, Vol. 30, No. 3, pp. 1-11.
  2. Bhattacharjee, P.S., Saha, D., and Mukherjee, A., An Approach for Location Area Planning in a Personal Communication Services Network(PCSN), IEEE Trans, On Wireless Communications, 2004, Vol. 3, No. 4, pp. 1176-1187. https://doi.org/10.1109/TWC.2004.830821
  3. Byeon, J., Jang, S., Kim, S., and Kim, Y., Optimal Design of Location Management Using Particle Swarm Optimization, Korean Management Science Review, 2012, Vol. 29, No. 1, pp. 143-152. https://doi.org/10.7737/KMSR.2012.29.1.143
  4. Chun, S.B., Woo, N.M., Yi, J.I., and Son, S.Y., Structural Equation Model for Customer Satisfaction Index for Ubiquitous Home Network System, Journal of the Korean Society for Quality Management, 2007, Vol. 35, No. 4, pp. 26-37.
  5. Demirkol, I., Ersoy, C., Caglayan, M.U., and Delic, H., Location Area Planning and cell-to-switch assignment in cellular networks, IEEE Trans, On Wireless Communications, 2004, Vol. 3, No. 3, pp. 880-890. https://doi.org/10.1109/TWC.2004.827767
  6. Hac, A. and Zhou, S., Locating strategies for personal communication networks : A novel tracking strategy, IEEE J, Selected Areas in Comm., 1997, Vol. 15, No. 8, pp. 1425-1436. https://doi.org/10.1109/49.634783
  7. Karaboga, D. and Akay, B., A comparative study of Artificial Bee Colony algorithm, Applied Mathematics and Computation, 2009, Vol. 214, No. 1, pp. 108-132. https://doi.org/10.1016/j.amc.2009.03.090
  8. Karaboga, D. and Bahriye, B., A powerful and efficient algorithm for numerical function optimization: artificial bee colony(ABC) algorithm, Journal of Global Optimization, 2007, Vol. 39, No. 3, pp. 459-471. https://doi.org/10.1007/s10898-007-9149-x
  9. Karaboga, D. and Basturk, B., On the performance of artificial bee colony(ABC) algorithm, Applied Soft Computing, 2008, Vol. 8, No. 1, pp. 687-697. https://doi.org/10.1016/j.asoc.2007.05.007
  10. Kim, S. and Byeon, J., Development of Improved Binary Artificial Bee Colony for Optimal Design of Reporting Cell Location Management System, Telecommunications Review, 2012, Vol. 22, No. 2, pp. 287-297.
  11. Kim, S., Byeon, J., Lee, S., and Liu, H., A grouping biogeography-based optimization for location area planning, Neural Computing and Applications, 2015, Vol. 26, No. 8, pp. 2001-2012. https://doi.org/10.1007/s00521-015-1856-5
  12. Kim, S., Kim, Y., and Kim, K., Location Area Planning using Ant Colony Optimization, Korean Management Science Review, 2008, Vol. 25, No. 2, pp. 73-80.
  13. Merchant, A. and Sengupta, B., Assignment of Cells to Switches in PCS Networks, IEEE/ACM Trans, On Networking, 1995, Vol. 3, No. 5, pp. 521-526. https://doi.org/10.1109/90.469954
  14. Pierre, S. and Houeto, F., Assigning Cells to Switches in Cellular Mobile Networks Using Taboo Search, IEEE Trans, on Systems, Man, and Cybernetics Part B : Cybernetics, 2002, Vol. 32, No. 3, pp. 351-356. https://doi.org/10.1109/TSMCB.2002.999810
  15. Prajapati, N.B. and Kathiriya, D.R., Dynamic location area planning in cellular network using Apriori algorithm, 2015 ICIC, India, 2015, pp. 660-662.
  16. Quintero, A. and Pierre, S., Evolutionary approach to optimize the assignment of cells to switches in personal communication networks, Computer Communications, 2003, Vol. 26, No. 9, pp. 927-938 https://doi.org/10.1016/S0140-3664(02)00238-4
  17. Subrata, R. and Zomaya, A.Y., A comparison of three artificial life techniques for reporting cell planning in Mobile Computing, IEEE Trans, Parallel and Distributed Systems, 2003, Vol. 14, No. 2, pp. 142-153. https://doi.org/10.1109/TPDS.2003.1178878
  18. Taheri, J. and Zomaya, A.Y., A Simulated Annealing approach for mobile location management, Computer Communications, 2007, Vol. 30, No. 4, pp. 714-730. https://doi.org/10.1016/j.comcom.2006.08.034