Soft Computing

, Volume 20, Issue 7, pp 2565–2575 | Cite as

A possibilistic approach to UMTS base-station location problem

Methodologies and Application

Abstract

In this paper, we address the problem of planning the universal mobile telecommunication system base stations location for uplink direction. The objective is to maximize the total traffic covered and minimize the total installation cost based on data involving fuzziness. To define the cost, researchers used the current period market prices as constants. However prices may change over time. Our aim here is to deal with the imprecise and uncertain information of prices. For this we introduce a model of problem where each cost is a fuzzy variable, and then we present a decision-making model based on possibility theory. To solve the problem we propose a search algorithm based on the hybridization of genetic algorithm and local search method. To validate the proposed method some numerical examples are given.

Keywords

UMTS Optimization Fuzzy variable Possibility theory Genetic algorithm Hybridization 

References

  1. Amaldi E, Capone A, Malucelli F (2003a) Planning UMTS base station location: optimization models with power control and algorithms. IEEE Trans Wirel Commun 2(5):939–952CrossRefGoogle Scholar
  2. Amaldi E, Capone A, Malucelli F, Signori F (2003b) Radio planning and optimization of W-CDMA systems. Pers Wirel Commun 2775:437–447CrossRefGoogle Scholar
  3. Amaldi E, Capone A, Malucelli F (2008) Radio planning and coverage optimization of 3G cellular networks. Wirel Netw. 14:435–447CrossRefGoogle Scholar
  4. Berruto E, Gudmundson M, Menolascino R, Mohr W, Pizarroso M (1998) Research activities on UMTS radio interface, network architectures, and planning. IEEE Commun Mag 36:82–95CrossRefGoogle Scholar
  5. Bontoux B (2008) Techniques hybrides de recherche exacte et approche: application des problmes de transport. Ph.D. thesis, University of Avignon and the VaucluseGoogle Scholar
  6. Coello CAC (2010) List of references on evolutionary multiobjective optimization. http://www.lania.mx/ccoello/EMOO/EMOObib.html
  7. Dreo J, Petrowski A, Siarry P, Taillard E (2003) Metaheuristiques pour l’optimisation difficile. Eyrolles, ParisGoogle Scholar
  8. Dubois D, Prade H (1980) Fuzzy sets and systems. Academic Press, New YorkMATHGoogle Scholar
  9. Gabli M, Jaara EM, Mermri EB (2013) Planning UMTS base station location using genetic algorithm with a dynamic trade-off parameter. In: Lecture note of computer science, vol 7853. Springer, Heidelberg, pp 120–134Google Scholar
  10. Hashemi SM, Moradi A, Rezapour M (2008) An ACO algorithm to design UMTS access network using divided and conquer technique. Eng Appl Artif Intell 21:931–940CrossRefGoogle Scholar
  11. Hata M (1980) Empirical formula for propagation loss in land mobile radio services. IEEE Trans Veh Technol VT–29:317–325CrossRefGoogle Scholar
  12. Juttner A, Orban A, Fiala Z (2005) Two new algorithms for UMTS access network topology design. Eur J Oper Res 164:456–474CrossRefMATHGoogle Scholar
  13. Katagiri H, Mermri EB, Sakawa M, Kato K, Nishizaki I (2005) A possibilistic and stockastic programming approach to fuzzy random MST problem. IEICE Trans Inf Syst E88–D 8:1912–1919CrossRefGoogle Scholar
  14. Konak A, Coit DW, Smith AE (2006) Multi-objective optimization using genetic algorithms: a tutorial. Reliab Eng Syst Saf 91:992–1007CrossRefGoogle Scholar
  15. Kumar R, Parida PP, Gupta M (2002) Topological design of communication networks using multiobjective genetic optimization. In: Proceedings of the 2002 world congress on computational intelligence-WCCI’02, 12–17 May, 2002. Honolulu, HI, USA. IEEEGoogle Scholar
  16. Mermri EB, Katagiri H, Sakawa M, Ishii H (2009) A variance minimization model for fuzzy randon minimum spanning tree problems. Sci Math Jpn Online e–2009:539–550MATHGoogle Scholar
  17. Meunier H (2002) Algorithmes evolutionnaires paralleles pour l’optimisation multi objectif de reseaux de telecommunications mobiles. Ph.D. thesis, University of Sciences and Technologies, LilleGoogle Scholar
  18. Mundt T (2004) How much is a byte? A survey of costs for mobile data transmission. In: 04 proceedings of the winter international synposium on Information and communication technologies (WISICT) Google Scholar
  19. Naghshineh M, Katzela I (1996) Channel assignment schemes for cellular mobile telecommunication systems: a comprehensive survey. IEEE Pers Commun 3:10–31CrossRefGoogle Scholar
  20. Peng YJ, Soong BH, Wang LP (2004) Broadcast scheduling in packet radio networks using mixed tabu–greedy algorithm. Electron Lett 40(6):375–376CrossRefGoogle Scholar
  21. Sakawa M (1993) Fuzzy sets and interactive multiobjective optimization, vol 1. Plenum Press, New YorkCrossRefMATHGoogle Scholar
  22. Salcedo-Sanz S, Santiago-Mozos R, Bousono-Calzon C (2004) A hybrid Hopfield network-simulated annealing approach for frequency assignment in satellite communications systems. IEEE Trans Syst Man Cybern Part B 34:1108–1116CrossRefGoogle Scholar
  23. St-Hilaire M, Chamberland S, Pierre S (2006) Uplink UMTS network design-an integrated approach. Comput Netw 50:2747–2761CrossRefMATHGoogle Scholar
  24. Thiel SU, Giuliani P, Ibbetson LJ, Lister D (2002) An automated UMTS site selection tool. In: 3rd international conference on 3G mobile communication technologies, pp 69–73Google Scholar
  25. Wang LP, Li S, Tian F, Fu X (2004) A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing. IEEE Trans Syst Man Cybern Part B-Cybern 34(5):2119–2125CrossRefGoogle Scholar
  26. Wang LP, Shi H (2006) A gradual noisy chaotic neural network for solving the broadcast scheduling problem in packet radio networks. IEEE Trans Neural Netw 17(4):989–1000CrossRefGoogle Scholar
  27. Wang LP, Liu W, Shi H (2008) Noisy chaotic neural networks with variable thresholds for the frequency assignment problem in satellite communications. IEEE Trans Syst Man Cybern Part C-Rev Appl 38(2):209–217CrossRefGoogle Scholar
  28. Wang LP, Liu W, Shi H (2009) Delay constrained multicast routing using the noisy chaotic neural networks. IEEE Trans Comput 58(1):82–89MathSciNetCrossRefGoogle Scholar
  29. Wu Y, Pierre S (2003) Optimization of access network design in 3G networks. Can Conf Electr Comput Eng 2:781–784Google Scholar
  30. Yang Y (2003) UMTS investment study. In: Technical report T-109.551, Telecommunication business II, Helsinki University, HelsinkiGoogle Scholar
  31. Zadeh LA (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 1:3–28MathSciNetCrossRefMATHGoogle Scholar
  32. Zadeh LA (2008) Is there a need for fuzzy logic? Inf Sci 178:2751–2779MathSciNetCrossRefMATHGoogle Scholar
  33. Zdunek R, Ignor T (2010) UMTS base station location planning with invasive weed optimization. Lect Notes Comput Sci 6114:698–705CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Mathematics and Computer Science, Faculty of ScienceUniversity Mohammed PremierOujdaMorocco

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