Net Centric Optimization    (OPLINK::UMA)

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New Research in Nature Inspired Algorithms for
Mobility Management in GSM Networks

Enrique Alba, José García-Nieto*, Javid Taheri and Albert Zomaya

 Description (Abstract):

Mobile Location Management (MLM) is an important and complex telecommunication problem found in mobile cellular GSM networks. Basically, this problem consists in optimizing the number and location of paging cells to find the lowest location management cost. There is a need to develop techniques capable of operating with this complexity and used to solve a wide range of location management scenarios. Nature inspired algorithms are useful in this context since they have proved to be able to manage large combinatorial search spaces efficiently. The aim of this study is to assess the performance of two different nature inspired algorithms when tackling this problem. The first technique is a recent version of Particle Swarm Optimization based on geometric ideas. This approach is customized for the MLM problem by using the concept of Hamming spaces. The second algorithm consists of a combination of the Hopfield Neural Network coupled with a Ball Dropping technique. The location management cost of a network is embedded into the parameters of the Hopfield Neural Network. Both algorithms are evaluated and compared using a series of test instances based on realistic scenarios. The results are very encouraging for current applications, and show that the proposed techniques outperform existing methods in the literature.


 Test Network Instances:
  • TNs with 4 x 4 cells dimension:  [1]   [2]  [3]
  • TNs with 6 x 6 cells dimension:  [4]  [5]  [6]
  • TNs with 8 x 8 cells dimension:  [7]  [8]  [9]
  • TNs with 6 x 6 cells dimension:  [10] [11] [12]

 Related Papers:

J. Taheri and A.Y. Zomaya. Realistic simulations for studying mobility manage- ment problems. Int. Journal of Wireless and Mobile Computing, 1(8), 2005.

J. Taheri and A.Y. Zomaya. The use of a hopfield neural network in solving the mobility management problem. In IEEE/ACS International Conference on Pervasive Services, ICPS 2004, pages 141-150, Jul 2004.

A. Moraglio, C. Di Chio, and R. Poli. Geometric Particle Swarm Optimization. In 10th European conference on Genetic Programming (EuroGP 2007),, Abril 2007.

E. Alba, J. García-Nieto, L. Jourdan, and E.-G.Talbi. Gene Selection in Cancer Classification using PSO/SVM and GA/SVM Hybrid Algorithms. In IEEE Congress on Evolutionary Computation CEC-07, Singapore, Sep 2007.

Click here to get the bibliography in bibtex format.
Last Updated: 10/01/09                                          * For any question or suggestion, contact with J. García-Nieto.
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