Artificial Neural Network Approach to Mobile Location Estimation in GSM Network

• Autorzy: Ezema L. S. , Ani C. I.

Identyfikatory

DOI

10.1515/eletel-2017-0006

• Języki publikacji: EN

Abstrakty

EN

The increase in utilisation of mobile location-based services for commercial, safety and security purposes among others are the key drivers for improving location estimation accuracy to better serve those purposes. This paper proposes the application of Levenberg Marquardt training algorithm on new robust multilayered perceptron neural network architecture for mobile positioning fitting for the urban area in the considered GSM network using received signal strength (RSS). The key performance metrics such as accuracy, cost, reliability and coverage are the major points considered in this paper. The technique was evaluated using real data from field measurement and the results obtained proved the proposed model provides a practical positioning that meet Federal Communication Commission (FCC) accuracy requirement.

Słowa kluczowe

EN

neural network; artificial intelligent; Levenberg-Marquardt; multi-layered perceptron; mobile location estimation; GSM

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2017
• Tom: Vol. 63, No. 1
• Strony: 39--44
• Opis fizyczny: Bibliogr. 13 poz., wykr.

Twórcy

autor

Ezema L. S.

• Department of Electronics Engineering, University of Nigeria Nsukka, Enugu State, Nigeria

autor

Ani C. I.

• Electronic Engineering, University of Nigeria Nsukka, Enugu State, Nigeria

Bibliografia

• [1] M. B. Zeytinci, V. Sari, F. K. Harmanci, E. Anarim and M. Akar, “Location Estimation Using RSS Measurements with Unknown Path Loss Exponents”, EURASIP Journal on Wireless Communications and Networking, 2013
• [2] L.S. Ezema and C.I. Ani, “Multiple Linear Regression Model for Mobile Location Estimation in GSM Urban Environments”, Indian Journal of Science and Technology; Feb., 2016
• [3] M. Samiei, M. Mehrjoo and B. Pirzade, “Advances of Positioning Methods in cellular Networks”, International Conference on Communications Engineering”, December 2010
• [4] L.S. Ezema, C.I. Ani and G.N. Ezeh, “Mobile Location Estimation in GSM/UMTS”, International Journal of Emerging Technology & Research, Vol. 1, Issue 3, Mar-Apr, 2014
• [5] H. Demuth, M. Beale and M. Hagan, ‘Neural Network Toolbox 6 User’s Guide’ The MathWorks, Inc, MA, US, March, 2009
• [6] C. M. Takenga and K. Kyamakya, ‘Location Fingerprinting in GSM Network and Impact of Data Preprocessing’,
• [7] M. Stella, M. Russo, and D. Begusic, ‘GSM-Based Approach for Indoor Localisation’, World Academy of Science, Engineering and Technology, 2013
• [8] J. Muhammad, ‘Artificial Neural Networks for location Estimation and Co-channel Interference Suppression in Cellular Networks’, February, 2007
• [9] S. Haykin, ‘Neural Networks: A Comprehensive foundation’, 2nd Edition, Prentice Hall, 1998
• [10] A. Hussain, ‘Novel Artificial Neural Network Architecture and Algorithms for Non-Linear Dynamical Systems Modeling and Digital communications Applications’, PhD Thesis, 1996
• [11] J. Costabile, ‘Wireless Position Location’, Virginia tech Wireless symposium, June 4, 2010.
• [12] J. Venkata Subramanian, and M. Abdul Karim Sadiq, “Implementation of Artificial Neural Network for Mobile Movement Prediction”, Indian Journal of science and Technology; 7(6), pp. 858-863, June 2014
• [13] Zoran Salcic, “AGPCS – An Automatic GSM-based Positioning and Communication System”, Second annual conference of GeComputation, University of Otago, New Zealand, 26-29 August 1997

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).