Abstract
In this paper, various parameters of cellular base station (BS) placement problem such as site coordinates, transmitting power, height and tilt angle are determined using evolutionary multiobjective algorithm to obtain better compromised solutions. The maximization of service coverage and minimization of cost are considered as conflicting objectives by satisfying inequality constraints such as handover, traffic demand and overlap. For the purpose of simulation, a 15 × 15 Km2 synthetic test system is discretized as hexagonal cell structure and necessary simulations are carried out to calculate receiving field strength at various points. The path loss is calculated using Hata model. To improve the diversity and uniformity of the obtained nondominated solutions, controlled elitism and dynamic crowding distance operators are introduced in non-dominated sorting genetic algorithm-II (NSGA-II) and are designated as modified NSGA-II (MNSGA-II). The optimal placement for BS is determined using MNSGA-II and NSGA-II. The effect of maximum number of function evaluations, handover and overlap on the performances of the algorithms is studied. A better distributed Pareto-front is obtained in MNSGA- II when compared with NSGA- II. The results reveal that, increasing of overlap percentage not only increases the coverage but also increases the overlap and handover error. The coverage percentage is indirectly proportional to the number of antennas involved in the handover constraint. The simulation results reveal that the proposed technique is more suitable for real-world BS placement problem.
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References
Rappaport, T. S. (2001). Wireless communications principles & practice (2nd ed.). NewJersy: Prentice-Hall.
Hurley, S. (2002). Planning effective cellular mobile radio networks. IEEE Transactions on Vehicular Technology, 51(2), 243–253.
Anderson, H. & McGeehan, J. P. (1994). Optimizing micro cell base station locations using simulated annealing techniques. In Proceedings of the IEEE 44th vehicular technology conference, Stockholm (pp. 858–862).
Tutschuka, K., Gerlich, N., Tran-Gia, P. (1997). An integrated cellular network planning tool. In Proceedings of the IEEE 47th vehicular technology conference (pp. 765–769).
Roullier-Callaghan, A. (2001). A radio coverage and planning tool. High Frequency Postgraduate Student Colloquium, 35–40.
Caminada, A., Dony, T., Morlier, JF., Mourniac, S., Altman, Z., et al. (2002). OaSys: FTR&D UMTS, automatic cell planning tool. In Proceedings of the IEEE antennas and propagation society international symposium (pp. 338–341).
Huang, X. & Behr, U. (2000). Automatic base station placement and dimensioning for mobile network planning. Proceedings of the IEEE 51st Vehicular Technology Conference (pp.1544–1549).
Mathar, RM., & Niessen, T. (2000). Optimum positioning of base stations for cellular radio networks. Journal of Wireless Networks, Springer, 6(6), 421–428.
Kling R. –M. & Banerjee P. (1987). ESP: new standard cell placement package using simulated evolution. ACM/IEEE 24th design automation conference (pp. 60–66).
Vasquez, M., & Hao, J. -K. (2001). A Heuristic approach for antenna positioning in cellular networks. Journal of Heuristics, 7(5), 443–472.
Zimmermann, J., Hons, R., & Muhlenbein, H. (2003). ENCON: an evolutionary algorithm for the antenna placement problem. Journal of Computers and Industrial Engineering, 44(2), 209–226.
Weicker, N., Szabo, G., Weicker, K., & Widmayer, P. (2003). Evolutionary multiobjective optimization for BS transmitter placement with frequency assignment. IEEE Transactions on Evolutionary Computation, 7(2), 189–203.
Calegarie, P., Guidec, F., Kuonen, P., Chamaret, B., Udeba, S., Josselin, S., & Wagner. (1996). Radio network planning with combinatorial algorithms. ACTS Mobile Communication, 707–713.
Molina, A., Athanasiadou, GE., Nix, AR. (1999). Automatic location of base-stations for optimized Cellular coverage: a new combinatorial approach. In Proceedings of the IEEE 49th Vehicular Technology conference (pp. 606–610).
Rawnsley K., & Hurley, S. (2000). Towards automatic cell planning. In Proceedings of the IEEE 11th personnel indoor and mobile radio communication symposium (pp. 1583–1588).
Allen, SM., Hurley, S., Taplin, RK., Whitaker, RM. (2001). Automatic cell planning of broad band fixed wireless networks. In Proceedings IEEE 53rd vehicular technology conference, 4, pp. 2808–2812.
Whitaker, R. M., & Hurley, S. (2005). On the optimality of facility location for wireless transmission infrastructure. Journal of Computers and Industrial Engineering, 46(1), 171–191.
Whitaker, R. M., Raisanen, L., & Hurley, S. (2005). The infrastructure efficiency of cellular wireless networks. Journal of Computer Networks, 48(6), 941–959.
Raisanen, L., Whitaker, RM., Hurley, S. (2004). A comparison of randomized and evolutionary approaches for optimizing BS site selection. In Proceedings ACM symposium on applied computing (pp. 1159–1165).
Raisanen, L., & Whitaker, R. M. (2005). Comparison and evaluation of multiple objective genetic algorithms for the antenna placement problem. Journal of Mobile and Network Applications, 10(1/2), 79–88.
Raisanen, L. (2008). A permutation-coded evolutionary strategy for multi-objective GSM network planning. Journal of Heuristics, 14(1), 1–21.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. New York: Wiley.
Biao Luo., Jinhua Zheng., Jiongliang Xie., Jun Wu. (2008). Dynamic crowding distance? A new diversity maintenance strategy for MOEAs. In Proceedings of the 4th international conference on natural compting, 1, 580–585.
Jeyadevi, S., et al. (2010). Solving multiobjective optimal reactive power dispatch using modified NSGA-II. International Journal of Electrical Power and Energy Systems, accepted for publication.
Hata, M. (1980). Empirical formula for propagation loss in land-mobile radio service. IEEE Transaction on Vehicular Technology, 29(3), 317–325.
Walfisch, J., & Bertoni, H. (1988). A theoretical model of UHF propagation in urban environments. IEEE Transaction on Antennas Propagation, 36(12), 1788–1796.
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist nondominated sorting genetic algorithm for multi-objective optimization NSGA-II. Lecture Notes in Computer Science 848–849.
Deb, K., & Goel, T. (2001). Evolutionary multi-criterion optimization. Berlin: Springer.
Grosan, C., et al. (2003). Performance metrics for multiobjective optimization evolutionary algorithms. In Proceedings conference on applied and industrial mathematics, CAIM, Oradea.
Deb, K. & Jain, S. (2002). Running performance metrics for evolutionary multi-objective optimization. In Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning, SEAL’02, Singapore, 13–20.
Qin, A. K., et al. (2009). Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans on Evolutionary Computation, 13(2), 398–417.
Acknowledgments
The authors are grateful to the managements of the Thiagarajar College of Engineering, Madurai and the K.L.N. College of Engineering, Madurai for having granted permission to utilize their infrastructure facilities for the research activities. The authors are also grateful to Dr.Stephen Hurley, Reader, Department of computer science and Director of the centre of mobile communications, Cardiff University, Wales, UK for his expert guidance. The authors also show their gratitude to Bharat Sanchar Nigam Limited, Madurai for having rendered many useful discussions and provided technical clarifications.
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Lakshminarasimman, N., Baskar, S., Alphones, A. et al. Evolutionary multiobjective optimization of cellular base station locations using modified NSGA-II. Wireless Netw 17, 597–609 (2011). https://doi.org/10.1007/s11276-010-0299-2
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DOI: https://doi.org/10.1007/s11276-010-0299-2