Optimisation of CDMA-Based Mobile Telephone Networks: Algorithmic Studies on Real-World Networks

  • Paul Weal
  • David Corne
  • Chris Murphy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


CDMA and WCDMA mobile phone networks depend on a network of antennae, each defining a geographic ‘cell’ that handles the transmissions to and from users’ handsets within that cell. These antennae have adjustable settings whose values have a large effect on both quality of service (and consequent subscriptions) and resource consumption. We consider the optimisation of these parameters, and describe experiments that compare a range of optimisation algorithms with the methods currently used in the field for this purpose. The aim of the current project was to achieve faster (necessary) and better (if possible) results than the existing methods used by field engineers. We find that certain evolutionary algorithm configurations achieve both of these requirements on test problems arising from real data from a high-traffic urban environment. To some extent the ideal algorithm depends on the size and load in the network being optimised, and this is the main topic of ongoing research.


Genetic Algorithm Voice User Gaussian Mutation Pilot Power Field Engineer 


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  1. 1.
    Bedakar, A., Borst, S., Ramanan, K., Whiting, P., Yeh, E.: Downlink scheduling in CDMA data networks. In: Proc. Global Telecomms. Conf., GLOBECOM 1999, vol. 5, pp. 2653–2657 (1999)Google Scholar
  2. 2.
    Moustafa, M., Habib, I., Naghshineh, M.: Wireless resource management using genetic algorithm for mobiles equilibrium. In: Proc. 6th IEEE Symp. on Computers and Comms., pp. 586–591 (2001)Google Scholar
  3. 3.
    Akl, R.G., Hegde, M.V., Naraghi-Pour, M., Min, P.S.: Multicell CDMA network design. IEEE Transactions on Vehicular Technology 50(3), 711–722 (2001)CrossRefGoogle Scholar
  4. 4.
    Chen, S., Luk, B.L.: Adaptive simulated annealing for optimization in signal processing applications. Signal Processing 79(1), 117–128 (1999)MATHCrossRefGoogle Scholar
  5. 5.
    Lee, C.Y., Kang, H.G.: Cell planning with capacity expansion in mobile communications: a tabu search approach. IEEE Transactions on Vehicular Technology 49(5), 1678–1691 (2000)CrossRefGoogle Scholar
  6. 6.
    Maple, C., Guo, L., Zhang, J.: Parallel Genetic Algs. for Third Generation Mobile Network Planning. In: Proc. Int’l. Conf. on Parallel Comp. in Elec. Eng (PARELEC 2004), pp. 229–236 (2004)Google Scholar
  7. 7.
    Love, R.T., Beshir, K.A., Schaeffer, D., Nikides, R.S.: A pilot optimization technique for CDMA cellular systems. In: Proc. 50th IEEE VTS Vehicular Technology Conf., vol. 4, pp. 2238–2242 (1999)Google Scholar
  8. 8.
    Sinclair, M.: The application of a genetic algorithm to trunk network routing table optimisation. In: Proc. 10th UK Teletraffic Symp.: Performance Engineering in Telecomms. Networks, pp. 2/1—2/6 (1993)Google Scholar
  9. 9.
    Abuali, F.N., Schoenefeld, D., Wainwright, R.L.: Designing telecommunications networks using genetic algorithms and probabilistic minimum spanning trees. In: Proceedings of the 1994 ACM symposium on Applied computing, pp. 242–246 (1994)Google Scholar
  10. 10.
    Celli, G., Costamagna, E., Fanni, A.: Genetic algorithms for telecommunication network optimization. In: Proc. IEEE Int’l. Conf. on Systems, Man and Cybernetics, vol. 2, pp. 1227–1232 (1995)Google Scholar
  11. 11.
    Kumar, A., Pathak, R.M., Gupta, Y.P.: Genetic-algorithm-based reliability optimization for computer network expansion. IEEE Transactions on Reliability 44(1) (1995)Google Scholar
  12. 12.
    Ko, K.-T., Tang, K.-S., Chan, C.-Y., Man, K.-F., Kwong, S.: Using genetic algorithms to design mesh networks. COMPUTER 30(8), 56–61 (1997)CrossRefGoogle Scholar
  13. 13.
    Webb, A., Turton, B.C.H., Brown, J.M.: Application of genetic algorithm to a network optimisation problem. In: Proc. 6th IEE Conference on Telecommunications, pp. 62–66 (1998)Google Scholar
  14. 14.
    Knowles, J., Oates, M., Corne, D.: Advanced Multi-Objective Evolutionary Algorithms Applied to Two Problems in Telecommunications. BT Technology Journal 18(4) (2000)Google Scholar
  15. 15.
    Hsinghua, C., Premkumar, G., Chao-Hsien, C.: Genetic algs. for commus. network design - an empirical study of the factors that influence performance. IEEE Trans. E.C. 5(3), 236–249 (2001)CrossRefGoogle Scholar
  16. 16.
    Weicker, N., Szabo, G., Weicker, K., Widmayer, P.: Evolutionary multiobjective optimization for base station transmitter placement with frequency assignment. IEEE Trans. E.C. 7(2), 189–203 (2003)CrossRefGoogle Scholar
  17. 17.
    Crepu, J.-C., Koukam, A., Lissajoux, T., Caminada, A.: Automatic mesh generation for mobile network dimensioning using evolutionary approach. IEEE Trans. Evol. Comp. 9(1), 18–30 (2005)CrossRefGoogle Scholar
  18. 18.
    Corne, D., Smith, G.D., Oates, M. (eds.): Telecommunications Optimization: Heuristic and Adaptive Techniques, p. 416. John Wiley & Sons, Chichester (2000)Google Scholar
  19. 19.
    Pedrycz, W. (ed.): Computational Intelligence in Telecommuns. Networks, p. 450. CRC Press, Boca Raton (2000)Google Scholar
  20. 20.
    Wang, L. (ed.): Soft Computing in Communications, p. 408. Springer, Heidelberg (2003)Google Scholar
  21. 21.
    Deb, K.: Multiobjective Optimization using Evolutionary Algorithms, p. 518. Wiley, Chichester (2001)Google Scholar
  22. 22.
    Corne, D., Deb, K., Fleming, P., Knowles, J.: The good of the many outweighs the good of the one: evolutionary multiobjective optimization, coNNectionS. IEEE Neur. Net. Soc. 1(1), 9–13 (2003)Google Scholar
  23. 23.
    Collete, Y., Siarry, P.: Multiobjective optimization: principles & case studies, p. 293. Springer, Heidelberg (2004)Google Scholar
  24. 24.
    Goldberg, D.: Genetic algs. in search, optimization and machine learning. Addison Wesley, Reading (1989)Google Scholar
  25. 25.
    Syswerda, G.: A study of reproduction in steady state genetic algorithms. In: FOGA I, pp. 94–101. Morgan Kaufmann, San Francisco (1991)Google Scholar
  26. 26.
    Syswerda, G.: Uniform crossover in genetic algorithms. In: Proc. 3rd Int’l Conf. on Genetic Algorithms, pp. 2–9. Morgan Kaufmann, San Francisco (1989)Google Scholar
  27. 27.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Service Center (1995)Google Scholar
  28. 28.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Science, 220(4598), 671—680 (1983)Google Scholar
  29. 29.
    Edgington, E.S.: Randomization Tests, p. 424. Marcel Dekker, New York (1995)MATHGoogle Scholar
  30. 30.
    Corne, D., Oates, M., Kell, D.: Landscape state machines: tools for evolutionary algorithm performance analyses and landscape /algorithm mapping. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 187–198. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  31. 31.
    Rowe, W., Corne, D., Knowles, J.: Predicting Stochastic Search Algorithm Performance using Landscape State Machines. Proc. IEEE Con. Evol. Comp. (to appear, 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Paul Weal
    • 1
  • David Corne
    • 2
  • Chris Murphy
    • 3
  1. 1.SECaMUniversity of ExeterUK
  2. 2.MACSHeriot-Watt UniversityEdinburghUK
  3. 3.Motorola LtdSwindon, WiltshireUK

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