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Performance Analysis of Cellular Radio System Using Artificial Neural Networks

Received: 26 December 2016     Accepted: 6 January 2017     Published: 17 March 2017
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Abstract

In this paper, we exploit one of the fastest growing techniques of Soft Computing, i.e. Artificial Neural Networks (ANNs) for obtaining various performance measures of a cellular radio system. A prioritized channel scheme with subrating is considered in which a fixed number of channels are reserved for handoff calls and in case of heavy traffic, these reserved channels are subrated into two channels of equal frequency to deal with more handoff calls. Two models dealing with infinite and finite number of subscribers are considered and the blocking probabilities of new and handoff calls are computed analytically as well as by using ANNs. A feedforward two-layer ANN is considered for obtaining the blocking probabilities. The backpropagation algorithm is used for training the ANN. The analytical and ANN results are compared by taking the numerical illustrations.

Published in American Journal of Neural Networks and Applications (Volume 3, Issue 1)
DOI 10.11648/j.ajnna.20170301.12
Page(s) 5-13
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Artificial Neural Networks, Cellular Radio System, Handoff, Reserved Channels, Subrating, Backpropagation

References
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[16] Fu, X., Bourgeois, A. G., Fan, P. and Pan, P. (2006). Using a genetic algorithm approach to solve the dynamic channel-assignment problem, Int. J. Mobile Communications, 4 (3).
[17] Khanbary, L. M. O. and Vidyarthi, D. P. (2009). Channel allocation in cellular network using modified genetic algorithm, International Journal of Artificial Intelligence, ISSN 0974-0635, 3 (A09).
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Cite This Article
  • APA Style

    Kriti Priya Gupta, Madhu Jain. (2017). Performance Analysis of Cellular Radio System Using Artificial Neural Networks. American Journal of Neural Networks and Applications, 3(1), 5-13. https://doi.org/10.11648/j.ajnna.20170301.12

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    ACS Style

    Kriti Priya Gupta; Madhu Jain. Performance Analysis of Cellular Radio System Using Artificial Neural Networks. Am. J. Neural Netw. Appl. 2017, 3(1), 5-13. doi: 10.11648/j.ajnna.20170301.12

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    AMA Style

    Kriti Priya Gupta, Madhu Jain. Performance Analysis of Cellular Radio System Using Artificial Neural Networks. Am J Neural Netw Appl. 2017;3(1):5-13. doi: 10.11648/j.ajnna.20170301.12

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  • @article{10.11648/j.ajnna.20170301.12,
      author = {Kriti Priya Gupta and Madhu Jain},
      title = {Performance Analysis of Cellular Radio System Using Artificial Neural Networks},
      journal = {American Journal of Neural Networks and Applications},
      volume = {3},
      number = {1},
      pages = {5-13},
      doi = {10.11648/j.ajnna.20170301.12},
      url = {https://doi.org/10.11648/j.ajnna.20170301.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20170301.12},
      abstract = {In this paper, we exploit one of the fastest growing techniques of Soft Computing, i.e. Artificial Neural Networks (ANNs) for obtaining various performance measures of a cellular radio system. A prioritized channel scheme with subrating is considered in which a fixed number of channels are reserved for handoff calls and in case of heavy traffic, these reserved channels are subrated into two channels of equal frequency to deal with more handoff calls. Two models dealing with infinite and finite number of subscribers are considered and the blocking probabilities of new and handoff calls are computed analytically as well as by using ANNs. A feedforward two-layer ANN is considered for obtaining the blocking probabilities. The backpropagation algorithm is used for training the ANN. The analytical and ANN results are compared by taking the numerical illustrations.},
     year = {2017}
    }
    

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    T1  - Performance Analysis of Cellular Radio System Using Artificial Neural Networks
    AU  - Kriti Priya Gupta
    AU  - Madhu Jain
    Y1  - 2017/03/17
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    DO  - 10.11648/j.ajnna.20170301.12
    T2  - American Journal of Neural Networks and Applications
    JF  - American Journal of Neural Networks and Applications
    JO  - American Journal of Neural Networks and Applications
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.ajnna.20170301.12
    AB  - In this paper, we exploit one of the fastest growing techniques of Soft Computing, i.e. Artificial Neural Networks (ANNs) for obtaining various performance measures of a cellular radio system. A prioritized channel scheme with subrating is considered in which a fixed number of channels are reserved for handoff calls and in case of heavy traffic, these reserved channels are subrated into two channels of equal frequency to deal with more handoff calls. Two models dealing with infinite and finite number of subscribers are considered and the blocking probabilities of new and handoff calls are computed analytically as well as by using ANNs. A feedforward two-layer ANN is considered for obtaining the blocking probabilities. The backpropagation algorithm is used for training the ANN. The analytical and ANN results are compared by taking the numerical illustrations.
    VL  - 3
    IS  - 1
    ER  - 

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Author Information
  • Symbiosis Centre for Management Studies, NOIDA Faculty of Management, Symbiosis International University, Pune, India

  • Department of Mathematics, Indian Institute of Technology (IIT), Roorkee, India

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