Abstract— the expansion plan of a Global System Mobile (GSM) network requires the analysis of some important variables known as key performance indicators (KPI) on the network. Network operators have tools for analyzing a KPI behavior on a particular network cell. This paper proposes a tool that illustrates graphically the behavior-in-time of a KPI in a whole geographical zone (including cell positions). A Gaussian process repressor is used over a real data set and time-space inference is performed. Finally we observe how a particular region presents high-KPI values most of the time. This alerts the network operator for including a solution in the formulation phase of the network expansion plan.
C. Steven and P. Samuel, “On the Expansion of Cellular Wireless Networks”. Éole Polytechnique de Montréal, 2002.
A.R. Mishra, Advanced Cellular Network Planning and Optimization: 2G/2.5G/3G… Evolution to 4G, John Wiley & Sons, 2006.
J. Laiho, A. Wacker, and T. Novosad, Radio Network Planning and Optimization for UMTS, 2001 :Wiley
C. Roberto, I. Tiziano and V. Luca. “Network monitoring and performance evaluation in a 3.5G network”. Computer Networks Vol. 51. 2007.
Sukkhawatchani, P.; Usaha, W., “Performance evaluation of anomaly detection in cellular core networks using self-organizing map,” Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008. ECTI-CON 2008. 5th International Conference, vol.1, no., pp.361, 364, 14-17 May 2008.
A.K.M. Fazlul, Mohamed Mir, K. Abu. “Performance Analysis of UMTS cellular Network using Sectorization Based on Capacity and Coverage” IJACSA International Journal of Advanced Computer Science and Applications Vol. 1, No.6, 2011.
Ye Ouyang and M. Hosein Fallah. “A performance Analysis for UMTS Packet Switched Network Based on Multivariate KPIs”. IJNGN International Journal of Next Generation Network, Vol.2, No.1, March 2010.
Kumpulainen P, Särkioja M. et al. “Analysing 3G radio network performance with fuzzy methods”. Neurocomputing Vol. 107. 2013.
C.E. Rasmussen and C. Williams, Gaussian Processes for Machine Learning. The MIT Press, 2006. 10 antes
J.Q.Shi, R. Murray-Smith and D.M. Titterington, “Hierarchical Gaussian Process Mixtures for Regression”. Statics and Computing, Springer, 2005.
A. Schwaighofer, M. Grigoras, V. Tresp and C. Hoffmann, “GPPS: A Gaussian Process Positioning System for Cellular Networks” in book: Advances in Neural Information Processing Systems 16. The MIT press, 2004.
B. Ferris, D. Hähnel and D. Fox, “Gaussian Processes for Signal Strength-Based Location Estimation”. Proc. of Robotics: Science and Systems, 2006.
Liutkus A. and Badeau R. and Richard G., “Multidimensional signal separation with Gaussian processes,” IEEE Statistical Signal Processing Workshop (SSP), 2011.
F. Duvallet and A.D. Tews, “WiFi Position Estimation in Industrial Environments Using Gaussian Processes”, Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on , vol., no., pp.2216,2221, 22-26 Sept. 2008
C.M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
K. P. Murphy, Machine Learning a Probabilistic Perspective. The MIT Press, 2012, p. 1067.
K. Chalupka, “Empirical evaluation of Gaussian Process approximation algorithms”, Master’s thesis, School of Informatics, University of Edinburgh, 2011