Multicriteria Optimization of Cellular Networks

Abstract

When designing modern cellular networks, it is challenging to account for many contradictory criteria and constantly changing external conditions of the networks (e.g., traffic). We need to solve multicriteria problems with high-dimensional vectors of parameters. A prerequisite to solution of these problems is correct determination of the feasible solution set, which is directly related to the statement of optimization problem. This is a major challenge in all multicriteria engineering optimization problems and represents significant difficulties for the expert. In this paper, we show how to define the feasible solution set for cellular network optimal design problems and thus answer the fundamental question of where to search for optimal solutions in such problems. We use the Parameter Space Investigation (PSI) method implemented in the Multicriteria Optimization and Vector Identification (MOVI) software system and apply it to a mathematical model of cellular network. In addition to developing methodology for stating and solving the problem of multicriteria optimization of cellular network, we have found that 1) defining the feasible solution set is directly related to the correct statement of the optimization problem, 2) once the feasible solution set has been determined, the criteria convolution can be applied to find the optimal solution in the feasible solution set, 3) it is possible to perform online tuning of the cellular network parameters.  

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R. Statnikov, J. Matusov, K. Pyankov and A. Statnikov, "Multicriteria Optimization of Cellular Networks," Open Journal of Optimization, Vol. 2 No. 3, 2013, pp. 53-60. doi: 10.4236/ojop.2013.23008.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] K. Miettinen, “Nonlinear Multiobjective Optimization,” Kluwer, Boston, 1999.
[2] J. Branke, K. Deb, K. Miettinen and R. Slowinski, “Multiobjective Optimization: Interactive and Evolutionary Approaches,” Springer-Verlag, Berlin, 2008. doi:10.1007/978-3-540-88908-3
[3] E. Melachrinoudis and B. Rosyidi, “Optimizing the Design of a CDMA Cellular System Configuration with Multiple Criteria,” Annals of Operations Research, Vol. 106, No. 1-4, 2001, pp. 307-329. doi:10.1023/A:1014574028174
[4] M. Galota, C. Glaβer, S. Reith and H. Vollmer, “A Polynomial-Time Approximation Scheme for Base Station Positioning in UMTS Networks,” Proceedings of the 5th International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications, Rome, 21 July 2001, pp. 52-59.
[5] E. Amaldi, P. Belotti, A. Capone and F. Malucelli, “Optimizing Base Station Location and Configuration in UMTS Networks,” Annals of Operations Research, Vol. 146, No. 1, 2006, pp. 135-151. doi:10.1007/s10479-006-0046-3
[6] S. Hurley, “Planning Effective Cellular Mobile Radio Networks,” IEEE Transactions on Vehicular Technology, Vol. 51, No. 2, 2002, pp. 243-253. doi:10.1109/25.994802
[7] A. Gerdenitsch, “System Capacity Optimization of UMTS FDD Networks,” PhD Thesis, Technische Universitat Wien, Wien, 2004.
[8] H. Zhu and T. Buot, “Multi-Parameter Optimization in WCDMA Radio Networks,” Vehicular Technology Conference, Milan, 17-19 May 2004, pp. 2370-2374.
[9] I. Siomina, “Radio Network Planning and Resource Optimization: Mathematical Models and Algorithms for UMTS, WLANs, and Ad Hoc Networks,” PhD Dissertation, Linkoping University, Linkoping, 2007.
[10] A. Jedidi, A. Caminada and G. Finke, “2-Objective Optimization of Cells Overlap and Geometry with Evolutionary Algorithms,” Applications of Evolutionary Computing, Lecture Notes in Computer Science, Vol. 3005, 2004, pp 130-139.
[11] V. Khare, X. Yao and K. Deb, “Performance Scaling of Multi-Objective Evolutionary Algorithms,” Proceedings of the Second Evolutionary Multi-Criterion Optimization (EMO-03) Conference (LNCS 2632), Faro, 8-11 April 2003, pp. 376390.
[12] Symena Software and Consulting GmbH. “CAPESSO 14.3 User Manual,” 2011.
[13] Forsk SA, “Atoll 2.8.3-User Manual,” 2010.
[14] U. Turke, “Efficient Methods for WCDMA Radio Network Planning and Optimization,” PhD Dissertation, TeubnerVerlag and DeutscherUniversitats-Verlag, Wiesbaden, 25 September 2007.
[15] H. F. Geerdes, “UMTS Radio Network Planning: Mastering Cell Coupling for Capacity Optimization,” PhD Dissertation, Technische Universitat Berlin, Berlin, 2008.
[16] K. Majewski and M. Koonert, “Conservative Cell Load Approximation for Radio Networks with Shannon Channels and Its Application to LTE Network Planning,” IEEE Sixth Advanced International Conference on Telecommunications, Barcelona, 9-15 May 2010.
[17] R. Statnikov and A. Statnikov, “The Parameter Space Investigation Method Toolkit,” Artech House, Boston/London, 2011.
[18] R. Statnikov and J. Matusov, “Multicriteria Analysis in Engineering,” Kluwer Academic Publishers, Dordrecht/ Boston/London, 2002. doi:10.1007/978-94-015-9968-9
[19] R. Statnikov and J. Matusov, “Multicriteria Optimization and Engineering,” Chapman & Hall, New York, 1995. doi:10.1007/978-1-4615-2089-4
[20] R. Statnikov, “Multicriteria Design. Optimization and Identification,” Kluwer Academic Publishers, Dordrecht/ Boston/London, 1999. doi:10.1007/978-94-017-2363-3
[21] I. M. Sobol’ and R. B. Statnikov, “Selecting Optimal Parameters in Multicriteria Problems,” 2nd Edition, Drofa, Moscow, 2006 (in Russian).
[22] R. B. Statnikov and J. B. Matusov, “Use of Nets for the Approximation of the Edgeworth-Pareto Set in Multicriteria Optimization,” Journal of Optimization Theory and Application, Vol. 91, No. 3, 1996, pp. 543-560. doi:10.1007/BF02190121
[23] R. Statnikov, J. Matusov and A. Statnikov, “Multicriteria Engineering Optimization Problems: Statement, Solution and Applications,” Journal of Optimization Theory and Applications, Vol. 155, No. 2, 2012, pp. 355-375. doi:10.1007/s10957-012-0083-9
[24] B. Suman and P. Kumar, “A Survey of Simulated Annealing as a Tool for Single and Multiobjective Optimization,” Journal of the Operational Research Society, Vol. 57, 2006, pp. 1143-1160. doi:10.1057/palgrave.jors.2602068

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