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Elman Recurrent Neural Network Application in Adaptive Beamforming of Smart Antenna System

by Adheed H. Sallomi, Sulaiman Ahmed
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 129 - Number 11
Year of Publication: 2015
Authors: Adheed H. Sallomi, Sulaiman Ahmed
10.5120/ijca2015907041

Adheed H. Sallomi, Sulaiman Ahmed . Elman Recurrent Neural Network Application in Adaptive Beamforming of Smart Antenna System. International Journal of Computer Applications. 129, 11 ( November 2015), 38-43. DOI=10.5120/ijca2015907041

@article{ 10.5120/ijca2015907041,
author = { Adheed H. Sallomi, Sulaiman Ahmed },
title = { Elman Recurrent Neural Network Application in Adaptive Beamforming of Smart Antenna System },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 129 },
number = { 11 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 38-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume129/number11/23121-2015907041/ },
doi = { 10.5120/ijca2015907041 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:23:10.648182+05:30
%A Adheed H. Sallomi
%A Sulaiman Ahmed
%T Elman Recurrent Neural Network Application in Adaptive Beamforming of Smart Antenna System
%J International Journal of Computer Applications
%@ 0975-8887
%V 129
%N 11
%P 38-43
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper an artificial Elman Recurrent Neural Network (ERNN) is used for smart antenna adaptive beamforming. Neural network is used to calculate the optimum weights of uniform linear array antenna that steer the radiation pattern of the antenna by directing multiple narrow beams toward the desired users and make nulling in the direction of unwanted users. Two different supervised training algorithms are used to train the ERNN , they are Levenberg Marquardt (LM) algorithm and Resilient Backpropagation (Rprop) algorithm. Uniform linear array is used with five element and the spacing between element equal to half wavelength .The results of ERNN training using LM and Rprop showed that the performance of Neural Network (NN) trained by LM training algorithm is better than Rprop training algorithm ,since it consider the fastest backpropagation training algorithm but it requires more memory than other algorithms.

References
  1. RK Jain, Sumit Katiyar and NK Agrawal, “Smart Antenna for Cellular Mobile Communication“ VSRD International Journal of Electrical, Electronics & Communication, Department of Electrical & Electronics Engineering, Singhania University, Jhunjhunu, Rajasthan, INDIA, Vol. 1 (9), 2011, pp.530-541.
  2. Hung Tuan Nguyen ,“Multiple Antenna Systems for Mobile Terminals”, PhD. Thesis , Department of Communication Technology., Aalborg University, Denmark, 2005.
  3. Jack H.Winters AT and T Labs, “Smart Antennas For Wireless Systems“ IEEE Personal Communication, ISSN :1070-9916 February 1998,pp.23-27.
  4. Frank Gross, “Smart Antenna for Wireless Communications “ , Mcgraw-hill, September, 2005.
  5. Murray, B.P. and Zaghloul, A.L. “Survey Of Cognitive Beamforming Techniques “ IEEE ,Dept. of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA, ISBN:978-1-4799-3119-4 ,January 2014
  6. Nwalozie G.C, Umeh K.C, Okorogu V.N and Oraetue C.D, “Performance Analysis of Constant Modulus Algorithm (CMA) Blind Adaptive Algorithm for Smart Antennas in a W-CDMA Network“, International Journal of Engineering Science and Innovative Technology (IJESIT), ISSN: 2319-5967, Volume 1, Issue 2, November ,2012 ,pp.246-254.
  7. Xu-Bao Shun and Shun-Shi Zhong, “An Adaptive Beamforming Approach Using Online Learning Neural Network“, IEEE, School of Communication and Information Engineering ,Shanghai University, China , ISBN:0-7803-8302-8, June 2004 ,pp.2663-2666.
  8. Ahmed H. El Zooghby, , Christos G. Christodoulou, and Michael Georgiopoulos, “A Neural Network Based Smart Antenna for Multiple Source Tracking “, IEEE , TRANSACTIONS ON ANTENNAS AND PROPAGATION, Electrical and Computer Engineering Department, University of Central Florida, Orlando, FL 32816 USA., VOL8, NO. 5, May 2000,pp.768-775.
  9. Halil Yigit , Adnan Kavak and H .Metin Ertunc “Using Autoregressive and Adaline Neural Network Modeling to Improve Downlink Performance of Smart Antennas“,IEEE, Department of Electronic and Computer Engineering, Kocaeli University, Izmit, Turkey, ISBN:0-7803-8599-3,June 2004,pp.165-170.
  10. Nuri Celik, , Wayne Kim, Mehmet F. Demirkol, Magdy F.Iskander, Rudy Emrick, “Implementation and Experimental Verification of Hybrid Smart-Antenna Beamforming Algorithm“,IEEE, Hawaii Center for Advanced Communication, University. of Hawaii, Honolulu, HI, VOL. 5, April 2006,pp280-283.
  11. Ross D. Murch and Khaled Ben Letaief, “Antenna Systems for Broadband Wireless Access “,IEEE Communications Magazine, Hong Kong University of Science and Technology, Department of Electrical and Electronic Engineeringof Science and Technology, China, ISSN :0163-6804, April 2002,pp76-83.
  12. Heikki Koivo and Mohammed Elmusrati , “Smart Antennas“,Systems Engineering in Wireless Communications, John Wiley and Sons, Ltd. ISBN: 978-0-470-02178-1,2009, ,pp 261-302 .
  13. Mohammad Tariqul Islam and zainol Abidin Abdul Rashid , “ MI-NLMS Adaptive Beamforming Algorithm For Smart Antenna System Applications“, Journal of Zhejiang University Science, Department of Electrical, Electronics and System Engineering, Faculty of Engineering, University of Kebangsaan Malaysia, ISSN 1009-3095,July 2006 pp.1709-1716 .
  14. Jian Li and Petre Stoica, “Robust Adaptive Beamforming “,J OHN WILEY & SONS, INC, ISBN: 10 0-471-67850-3,2005.
  15. D.M. Rodvold, D.G. McLeod, J.M. Brandt, P.B. Snow, and G.P. Murphy, “Introduction to Artificial Neural Networks for Physicians: Taking the Lid Off the Black Box“, Wiley-Liss,Inc., Volume 46, Issue 1,  January 2001, pp.39–44.
  16. A. H. El Zooghby, M. Georgiopoulos and C. G. Christodoulou , “Neural Network-Based Adaptive Beamforming for One- and Two-Dimensional Antenna Arrays“, IEEE, Electrical and Computer Engineering Department, University of Central Florida, Orlando, FL 32816 USA, December 1998 pp.1891-1893.
  17. Laurene Fausett, “ Fundamentals Of Neural Networks Architectures, Algorithms and Applications “,Prentice-Hall,ISBN: 0133341860, 9780133341867,1994 .
  18. Wai-Kai Chen, “Neural Networks and Computing Learning Algorithms and Applications“ , SERIES IN ELECTRICAL AND COMPUTER ENGINEERING, University of Illinois, Chicago, USA), ISBN-10 1-86094-758-1,2007.
  19. Medsker L.R. and L.C. Jain, “Recurrent Neural Networks Design And Application” ,CRC Press, Boca Raton London New York Washington, D.C, 2001.Graves A., Hinton G, and Mohamed A. . ,“Speech Recognition with Deep Recurrent Neural Networks” Department of Computer Science, Toronto University, PP.1-5, 2012.
  20. Ben Krose and Patrick van der Smagt,“An Introduction to Neural Networks”, 8th eddition, Amsterdam University, November 1996.
  21. Guez-Estrello , Carmen B. Rodr, and Felipe A. Cruz Pérez, “An Insight into the Use of Smart Antennas in Mobile Cellular Networks“ , University Campus ,Electric Engineering Department, ISBN 978-953-307-246-3,April 2011
Index Terms

Computer Science
Information Sciences

Keywords

Smart Antenna Conventional and Adaptive Beamforming Elman Recurrent Neural Network.

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