Autonomous Self-deployment of Wireless Access Networks in an Airport Environment

  • Holger Claussen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3854)


In environments with highly dynamic user demand, for example in airports, high over-dimensioning of wireless access networks is required to be able to serve high user densities at any possible location in the covered area, resulting in a large number of base stations. This problem is addressed with the novel concept of a self-deploying network. Distributed algorithms are proposed, which autonomously identify the need of changes in position and configuration of wireless access nodes and adapt the network to its environment. It is shown that a self-deploying network can significantly reduce the number of required base stations compared to a conventional statically deployed network. In this paper, this is demonstrated in a specific test scenario at Athens International Airport, simulating a moving user hotspot after the arrival of an airplane.


Wireless Access Network Pilot Power Base Station Location Conventional Network Mobile Base Station 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Mullany, F.J., Ho, L.T.W., Samuel, L.G., Claussen, H.: Self-deployment, self-configuration: Critical future paradigms for wireless access networks. In: Smirnov, M. (ed.) WAC 2004. LNCS, vol. 3457, pp. 58–68. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Hurley, S.: Planning effective cellular mobile radio networks. IEEE Transactions on Vehicular Technology 51(2), 243–253 (2002)CrossRefGoogle Scholar
  3. 3.
    Thiel, S.U., Giuliani, P., Ibbetson, L.J., Lister, D.: An automated UMTS site selection tool. In: Proc. 3G Mobile Communication Technologies, pp. 69–73 (2002)Google Scholar
  4. 4.
    Weicker, N., Szabo, G., Weicker, K., Widmayer, P.: Evolutionary multiobjective optimization for base station transmitter placement with frequency assignment. IEEE Transactions on Evolutionary Computation 7(2), 189–203 (2003)CrossRefGoogle Scholar
  5. 5.
    Mathar, R., Niessen, T.: Optimum positioning of base stations for cellular radio networks. Wireless Networks 6(6), 421–428 (2000)CrossRefMATHGoogle Scholar
  6. 6.
    Chandra, R., Qiu, L., Jain, K., Mahdian, M.: Optimizing the Placement of Integration Points in Multi-hop Wireless Networks. In: Proc. IEEE ICNP (2004)Google Scholar
  7. 7.
    Tutschku, K.: Demand-based radio network planning of cellular mobile communication systems. In: Proc. 17th Annual INFOCOM, pp. 1054–1061 (1998)Google Scholar
  8. 8.
    Abusch-Magder, D., Graybeal, J.M.: Novel algorithms for efficient exploration of the trade-off between cell count and performance in wireless networks. BLTJ 10(2) (2005)Google Scholar
  9. 9.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence – from natural to artificial systems. Oxford University Press, Oxford (1999)MATHGoogle Scholar
  10. 10.
    Shannon, C.E.: A mathematical theory of communication. BSTJ 27, 379–423, 623–656 (1948)Google Scholar
  11. 11.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical recipes in C++ - The art of scientific computing. Cambridge University Press, Cambridge (2002)MATHGoogle Scholar
  12. 12.
    Claussen, H.: Efficient modelling of channel maps with correlated shadow fading in mobile radio systems. In: Proc. IEEE International Symposium on Personal Indoor and Mobile Radio Communications PIMRC (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Holger Claussen
    • 1
  1. 1.Bell LaboratoriesLucent TechnologiesSwindonUnited Kingdom

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