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Multi-stage Neural Networks for Channel Assignment in Cellular Radio Networks

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Advances in Neural Networks - ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3174))

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Abstract

In this paper, we consider the channel assignment problem in cellular mobile communication systems, which assigns a channel to every requested call with the minimum span of channels subject to interference constraints. In this paper, a multi-stage heuristic algorithm using neural networks for the channel assignment problem is proposed. The proposed algorithm is devised to find first a good initial feasible assignment, then a locally optimal assignment, and finally a overall best quality assignment. The proposed algorithm has been applied to the Philadelphia benchmark instances. Experimental results show that the proposed method is competitive with the other existing algorithms.

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References

  1. Anderson, L.G.: A Simulation Study of Some Dynamic Channel Assignment Algorithms in a High Capacity Mobile Telecommunications System. IEEE Transactions on Communications 21, 1294–1301 (1973)

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Pohang University of Science and Technology, Pohang, Kyungbuk, 790-784, Korea

    Hyuk-Soon Lee, Dae-Won Lee & Jaewook Lee

Authors
  1. Hyuk-Soon Lee
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  2. Dae-Won Lee
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  3. Jaewook Lee
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Lee, HS., Lee, DW., Lee, J. (2004). Multi-stage Neural Networks for Channel Assignment in Cellular Radio Networks. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_45

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  • DOI: https://doi.org/10.1007/978-3-540-28648-6_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22843-1

  • Online ISBN: 978-3-540-28648-6

  • eBook Packages: Springer Book Archive

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