Performance Analysis of Cellular Radio System Using Artificial Neural Networks
American Journal of Neural Networks and Applications
Volume 3, Issue 1, February 2017, Pages: 5-13
Received: Dec. 26, 2016; Accepted: Jan. 6, 2017; Published: Mar. 17, 2017
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Authors
Kriti Priya Gupta, Symbiosis Centre for Management Studies, NOIDA Faculty of Management, Symbiosis International University, Pune, India
Madhu Jain, Department of Mathematics, Indian Institute of Technology (IIT), Roorkee, India
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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.
Keywords
Artificial Neural Networks, Cellular Radio System, Handoff, Reserved Channels, Subrating, Backpropagation
To cite this article
Kriti Priya Gupta, Madhu Jain, Performance Analysis of Cellular Radio System Using Artificial Neural Networks, American Journal of Neural Networks and Applications. Vol. 3, No. 1, 2017, pp. 5-13. doi: 10.11648/j.ajnna.20170301.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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