A Self-Organizing Channel Assignment Algorithm: A Cellular Learning Automata Approach

  • Hamid Beigy
  • M. R. Meybodi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2690)

Abstract

Introduction of micro-cellular networks offer a potential increase in capacity of cellular networks, but they create problems in management of the cellular networks. A solution to these problems is self-organizing channel assignment algorithm with distributed control. In this paper, we first introduce the model of cellular learning automata in which learning automata are used to adjust the state transition probabilities of cellular automata. Then a cellular learning automata based self-organizing channel assignment algorithm is introduced. The simulation results show that the micro-cellular network can self-organize by using simple channel assignment algorithm as the network operates.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Katzela, I., Naghshineh, M.: Channel Assignment Schemes for Cellular Mobile Telecommunication Systems: A Comprehensive Survey. IEEE Personal Communications, 10–31 (June 1996)Google Scholar
  2. 2.
    Hale, W.K.: Frequence Assignment:Theory and Applications. In: Proceedings of IEEE, vol. 68, pp. 1497–1514 (1980)Google Scholar
  3. 3.
    Narendra, K.S., Thathachar, K.S.: Learning Automata: An Introduction. Printice-Hall, New York (1989)Google Scholar
  4. 4.
    Srikantakumar, P.R., Narendra, K.S.: A Learning Model for Routing in Telephone Networks. SIAM Journal of Control and Optimization 20, 34–57 (1982)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Oommen, B.J., de St, E.V.: Graph Partitioning Using Learning Automata. IEEE Transactions on Commputers 45, 195–208 (1996)MATHCrossRefGoogle Scholar
  6. 6.
    Oommen, B.J., Roberts, T.D.: Continuous Learning Automata Solutions to the Capacity Assignment Problem. IEEE Transactions on Commputers 49, 608–620 (2000)CrossRefGoogle Scholar
  7. 7.
    Meybodi, M.R., Beigy, H.: A Note on Learning Automata Based Schemes for Adaptation of BP Parameters. Journal of Neuro Computing 48, 957–974 (2002)MATHGoogle Scholar
  8. 8.
    Meybodi, M.R., Beigy, H.: New Learning Automata Based Algorithms for Adaptation of Backpropagation Algorithm Parameters. International Journal of Neural Systems 12, 45–68 (2002)Google Scholar
  9. 9.
    Packard, N.H., Wolfram, S.: Two-Dimensional Cellular Automata. Journal of Statistical Physics 38, 901–946 (1985)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Krishna, K.: Cellular Learning Automata: A Stochastic Model for Adaptive Controllers. Master’s thesis, Department of Electrical Engineering, Indian Institue of Science, Banglore, India (June 1993)Google Scholar
  11. 11.
    Meybodi, M.R., Beigy, H., Taherkhani, M.: Cellular Learning Automata and its Applications Accepted for Publication in Journal of SharifGoogle Scholar
  12. 12.
    Beigy, H., Meybodi, M.R.: Cellular Learning Automata Based Self-Organizing Channel Assignment Algorithms. Tech. Rep. TR-CE-2002-008, Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hamid Beigy
    • 1
  • M. R. Meybodi
    • 1
  1. 1.Soft Computing Laboratory, Computer Engineering DepartmentAmirkabir University of TechnologyTehranIran

Personalised recommendations