Neural based RBF and LVQ Network Model of Knowledge Representation in the Prediction of Mobile Location

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    In mobile communication system, mostly the Location based services and quality of services have need of information about the mobile station location. If the cellular communication system knows the movement of the subscriber is preplanned, and exceedingly passionate about the mobile subscriber’s personal characteristics. Thus prediction of mobile location is mainly essential matter to give the location based quality of service to the subscribers [8]. Neural network has several network models that can be utilized to predict mobile location and preparation parameters can be collect from the individual portability of the subscriber. In this paper our contribution is that RBF network techniques and LVQ be use to forecast the subscriber’s next locality based on the present locality [6]. The MATLAB software was making use of substantiate the constraints of Radial Basis Function network structure and also the similar training facts to LVQ network. At first, the execution of the LVQ (Learning Vector Quantization), RBF (Radial Basis function) [13] has been considered. Our real commitment in this paper is that we prepared neural system utilizing the data about adjusting cell and neighboring cells, collected from a drive analyzer Reality mining on specific ways demonstrating the genuine Mobile Station (MS) area.

     

     


  • Keywords


    Neural network, LVQ, RBF, Mobile Location, Reality mining data.

  • References


      [1] Nathan Eagle, Alex Pentland, and David Lazer. Inferring Social Network Structure using Mobile Phone Data, Proceedings of the National Academy of Sciences (PNAS), 2009, Vol 106 (36), pp.15274-15278.

      [2] Kohonen, T., (1990). Statistical Pattern Recognition Revisited, Advanced Neural Computers R. Eckmiller (editor), 137-144.

      [3] Norton, C. A., and Zahorian, S. A. (1995). Speaker Verification with Binary -Pair Partitioned Neural Networks, ANNIE-95.

      [4] Zahorian S. A., Nossair, Z. B., and Norton, C. A., (1993). A Partitioned Neural Network Approach for Vowel Classification using Smoothed Time/Frequency Features, Eurospeech-93 , 1225-1228.

      [5] Pradeep Bilukar,Narasimha Rao, “Applications of neural network techniques for location prediction in mobile networking”, Proceedings of the 9th international conference on neural information processing, Vo1.5,pp.215-2161

      [6] Y. Zhao, "Standardization of mobile phone positioning for 3G systems,"IEEE Comm. Mag., pp. 108-1 16, July 2002.

      [7] J. Caffery and G. L. Stuber, "Subscriber location in CDMA cellular networks," IEEE Trans. Veh. Technol., vol. 41, pp. 406-416, May 1998.

      [8] M. A. Spirito, "On the accuracy of cellular mobile station location estimation," IEEE Trans. Veh. Technol., vol. 50, no. pp. 674-685, May2001.

      [9] M. McGuire, K. N. Plataniotis and A. N. Venetsanopoulos, "Estimating position of mobile terminals with survey data," EURASIP Journal onApplied Signal Processing, pp. 58-66, Jan. 2002.

      [10] www.ws.binghamton.edu

      [11] www.media.mit.edu

      [12] www.tutorialspoint.com

      [13] www.docplayer.net

      [14] Qiang Gao, A. Acampora. "Connection tree based micro-mobility management for IPcentric mobile networks", 2002 IEEE International Conference on Communications.Conference Proceedings. ICC 2002 (Cat.No.02CH37333), 2002.

      [15] Fenglian Liu. "An Improved RBF Network for Predicting Location in Mobile Network", 2009 Fifth International Conference on Natural Computation, 08/2009.

      [16] Vikas Chaurasia, Saurabh Pal, BB Tiwari."Prediction of benign and malignant breast cancer using data mining techniques", Journal of Algorithms & Computational Technology, 2018.

      [17] www.cs.columbia.edu

      [18] J. Laatu. "Mobility management in the third generation mobile network", Proceedings of GLOBECOM 96 1996 IEEE Global Telecommunications Conference GLOCOM-96,1996.

      [19] www.slideshare.net

      [20] Phan Anh Tan. "A Mobility Management and Routing Protocol Using Tree Architecture for Internet Connectivity of Mobile Ad Hoc Networks", 2007 16th International Conference on Computer Communications and Networks,08/2007.

      [21] Y. Kirsal. "An Analytical Approach for Performance Analysis of Handoffs in the Next Generation Integrated Cellular Networks and T. Roos, P. Myllymaki and H. Tim, "A statistical modeling approach to location estimation", IEEE Trans. on Mobile Computing, Vol. 1, pp. 59-69, January- March 2002.

      [22] H. L. Southall, J. A. Simmers and T. H. ODonnell, "Direction estimation in phased arrays with a neural network beamformer," IEEE Trans. Antennas Propagation., vol. 43, pp. 1369-1374, Dec. 1995.

      [23] A. H. El Zooghby, C. G. Christodoulou and M. Georgiopoulos,"Performance of radial-basis function networks for direction of arrival estimation with antenna arrays, " IEEE Trans. Antennas Propagation,vol. 45,no. 11,pp.1611-1617, Nov. 1997.

      [24] H. Zamiri-Jafarian, M. M. Mirsalehi, 1. Ahadi-Akhlaghi and H.Keshavarz, "A neural network-based mobile positioning with hierarchical structure," in Proc. 2003 IEEE Vehicular Technology Con$(VTC2003-Fall), pp. 2003 - 2007, May 2003.

      [25] P. D. Wassermann, Advanced Methods in Neural Computing. New York Van Nostrand Reinhold, 1993.


 

View

Download

Article ID: 22042
 
DOI: 10.14419/ijet.v7i4.19.22042




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.