Performance Analysis of Cellular Radio System Using Artificial Neural Networks

Kriti Priya Gupta; Madhu Jain

doi:doi:10.11648/j.ajnna.20170301.12

| Peer-Reviewed

Performance Analysis of Cellular Radio System Using Artificial Neural Networks

Kriti Priya Gupta, Madhu Jain

Published in American Journal of Neural Networks and Applications (Volume 3, Issue 1)

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Cichocki, A. and Unbehauen, R. (1993). Neural networks for optimization and signal processing, Wiley, NY, USA.
[2]	Clarkson, T. G., Ng, C. K. and Guan, Y. (1993). The pRAM: an adaptive VLSI chip, IEEE Trans. Neural Networks, Special Issue on Neural Network Hardware, 4 (3), 408-412.
[3]	Hopfield, J. J. and Tank, D. W. (1995). Neural computation of decisions in optimization problems, Biol. Cybern., 52, 141-152.
[4]	Onyiagha, C. G., Krasniqi, X. and Clarkson, T. G. (June 1996). Probabilistic RAM neural networks in an ATM multiplexer in solving engineering problems with neural networks, Proc. International Conference Engineering Applications of Neural Networks, 29-232.
[5]	Hecht-Nielsem, R. (Jan. 1989). Theory of back-propagation neural networks, Proc. IEEE International. Conf. Neural Networks, Washington, USA, 1, 593-605.
[6]	Hornik, K. (1989). Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359-366.
[7]	Tarraf, A. A., Habib, I. W. and Saadawi, T. N. (1993). Neural networks for ATM multimedia traffic prediction, Proc. International. Workshop on Applications of Neural Networks to Telecommunications, 1, 85-91.
[8]	Moh, W. M., Chen, M. J., Chu, N. M. and Liao, C. D. (1995). Traffic prediction and dynamic bandwidth allocation over ATM: a neural network approach, Comput. Commun., 18 (8), 563-571.
[9]	Drossu, R., Lakshman, T. V., Obradovic, Z. and Raghavendra, C. (1995). Single and multiple frame video traffic prediction using neural network models, Computer Networks Architectures and Applications, 146-158.
[10]	Edwards, T., Tansley, D. S. W., Frank, R. J. and Davey, N. (1997): Traffic trends analysis using neural networks, Proc. International. Workshop on Applications of Neural Networks to Telecommunications, 157-164.
[11]	Chang, P. R. and Hu, J. T. (1997). Optimal nonlinear adaptive prediction and modeling of MPEG video in ATM networks using pipelined recurrent neural networks, IEEE J. Sel. Areas Commun., 15 (6), 1087-1100.
[12]	Bolla, R., Davoli, F., Maryni, P. and Parisini, T. (Aug. 1998). An adaptive neural network admission controller for dynamic bandwidth allocation, IEEE Trans. Syst., Man, Cybern, B., Special Issue on Artificial Neural Networks, 28, 592-601.
[13]	Davoli, F. and Maryni, P. (Feb. 2000). A two level stochastic approximation for admission control and bandwidth allocation, IEEE J. Selec. Areas Commun., 18 (2), 222-233.
[14]	Balestrieri, F., Panteli, L. P., Dionissopoulos, V. and Clarkson, T. G. (2000). ATM connection admission control using pram based artificial neural networks, Computer Networks, 34, 49-63.
[15]	Lin, P. and Lin, Y. B. (2001). Channel allocation for GPRS, IEEE Trans. Veh. Tech., 50 (2), 375-387.
[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).
[18]	Siddesh. G. K, Muralidhara, K. N., Manjula. N. H. (July 2011). Routing in ad hoc wireless networks using soft computing techniques and performance evaluation using hypernet simulator, International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, 1 (3).
[19]	Rajagopalan, N. and Mala, C. (2012). Optimization of QoS parameters for channel allocation in cellular networks using soft computing techniques, Advances in Intelligent and Soft Computing, 130, 621-631.
[20]	Jain, M. and Rakhee (2001). Queueing analysis for PCS with integrated traffic and sub-rating channel assignment scheme, Journal of CSI, 31 (2), 1-8.
[21]	Leca, C. L., Nicolaescu, L., and Rîncu, C. (2015). Significant Location Detection & Prediction in Cellular Networks using Artificial Neural Networks. Computer Science and Information Technology, 3, 81-89. doi: 10.13189/csit.2015.030305.
[22]	Silva, M., Carvalho, G., Monteiro, D., and Machad, L. S. (2015). Distributed Target Location in Wireless Sensors Network: An Approach Using FPGA and Artificial Neural Network, Wireless Sensor Network, 7, 35-42.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
T1  - Performance Analysis of Cellular Radio System Using Artificial Neural Networks
AU  - Kriti Priya Gupta
AU  - Madhu Jain
Y1  - 2017/03/17
PY  - 2017
N1  - https://doi.org/10.11648/j.ajnna.20170301.12
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
SP  - 5
EP  - 13
PB  - Science Publishing Group
SN  - 2469-7419
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  -

Copy | Download

Author Information

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
T1  - Performance Analysis of Cellular Radio System Using Artificial Neural Networks
AU  - Kriti Priya Gupta
AU  - Madhu Jain
Y1  - 2017/03/17
PY  - 2017
N1  - https://doi.org/10.11648/j.ajnna.20170301.12
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
SP  - 5
EP  - 13
PB  - Science Publishing Group
SN  - 2469-7419
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  -

Copy | Download