Cellular Network Fingerprint Localization Simulation: A Soft Computing Approach

Cellular Network Localization (CNL) is a vital source of information to a wide range of emerging applications, to name a few, Location based services, traffic analysis and sensing, vehicles' anti-theft systems. Developing, validating, and testing of such applications require massive data sets of CNL - a prerequisite that is impractical to be collected. Furthermore, it can not be simulated using hard computing models due to the lack of estimators that can ascribe accurate level of localization accuracy as to the change in various spatial properties. In this paper, we propose a soft computing model to simulate the CNL performance, called SCMCL. This model carries out the estimation of CNL performance according to a knowledge base built on the basis of empirical findings reported in the literature. SCMCL is fed with real-life vehicular traces and produces CNL. SCMCL is validated and demonstrated throughout various operation conditions.

[1]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[2]  L.J. Greenstein,et al.  A microcell/macrocell cellular architecture for low- and high-mobility wireless users , 1991, IEEE Global Telecommunications Conference GLOBECOM '91: Countdown to the New Millennium. Conference Record.

[3]  Mike Y. Chen,et al.  Practical Metropolitan-Scale Positioning for GSM Phones , 2006, UbiComp.

[4]  Tao Peng,et al.  Post-processing of Fingerprint Localization using Kalman Filter and Map-matching Techniques , 2007, The 9th International Conference on Advanced Communication Technology.

[5]  Anders Furuskar,et al.  Downtilted Base Station Antennas - A Simulation Model Proposal and Impact on HSPA and LTE Performance , 2008, 2008 IEEE 68th Vehicular Technology Conference.

[6]  Inkyu Lee,et al.  Sum Rates of Random Beamforming MISO Downlink Systems with Other Cell Interference , 2011, 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring).

[7]  Otman A. Basir,et al.  GPS Localization Accuracy Classification: A Context-Based Approach , 2013, IEEE Transactions on Intelligent Transportation Systems.

[8]  C. Takenga,et al.  A Low-cost Fingerprint Positioning System in Cellular Networks , 2007, 2007 Second International Conference on Communications and Networking in China.

[9]  Lawrence Wai-Choong Wong,et al.  Error analysis for fingerprint-based localization , 2010, IEEE Communications Letters.

[10]  Bay Zolt,et al.  A Hybrid Simulation Framework for Modeling and Analysis of Vehicular Ad Hoc Networks , 2011 .

[11]  Tiago Rosa Maria Paula Queluz,et al.  MIMO Antenna Array Impact on Channel Capacity for a Realistic Macro-Cellular Urban Environment , 2008, 2008 IEEE 68th Vehicular Technology Conference.

[12]  J. J. Caffery,et al.  A new approach to the geometry of TOA location , 2000, Vehicular Technology Conference Fall 2000. IEEE VTS Fall VTC2000. 52nd Vehicular Technology Conference (Cat. No.00CH37152).

[13]  Fakhreddine O. Karray,et al.  Soft Computing and Intelligent Systems Design, Theory, Tools and Applications , 2006, IEEE Transactions on Neural Networks.

[14]  Wee-Seng Soh,et al.  Cramer-Rao Bound Analysis of Localization Using Signal Strength Difference as Location Fingerprint , 2010, 2010 Proceedings IEEE INFOCOM.

[15]  James J. Caffery,et al.  Wireless Location in CDMA Cellular Radio Systems , 1999 .

[16]  Peter Brida,et al.  Impact of the number of access points in indoor fingerprinting localization , 2010, 20th International Conference Radioelektronika 2010.

[17]  Dragan Kukolj,et al.  Indoor fingerprint localization in WSN environment based on neural network , 2011, 2011 IEEE 9th International Symposium on Intelligent Systems and Informatics.