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PhD Thesis Proposal : A Real Time Mobile Station Location Estimation Based on Artificial Intelligence Modelling Approach with Application to TETRA system.

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Presentation on theme: "PhD Thesis Proposal : A Real Time Mobile Station Location Estimation Based on Artificial Intelligence Modelling Approach with Application to TETRA system."— Presentation transcript:

1 PhD Thesis Proposal : A Real Time Mobile Station Location Estimation Based on Artificial Intelligence Modelling Approach with Application to TETRA system BY Faihan Al- Otaibi First Semester 1425 - 1426 (2004 - 2005)

2 Proposal Contents: Research Topic. Previous Studies. Research Objectives. Research Methodology. Preliminary Thesis outline. Research Time Plane. References.

3 Research Topic : 1- Wireless Location Positioning Systems Deployment : Before 1996 Military & Marine applications. For public cellular networks: 1996: FCC issued E911 Mandate phase I. PSAP accuracy requirements phase II: 50 - 100 meters accuracy for at least 67% of emergency calls. 150 - 300 meters accuracy for at least 95% of emergency calls Recently: EU passed E112 Mandate in Europe. This year: RFQ of Location – Based Services (LBS) have been invited by STC. STC adopted the FCC accuracy ranges. For private cellular networks: 2001: Some big organizations & enterprises started to adapt their networks to LBS. Recently : A - GPS has been used for some private networks. Industry analysts have forecasted that the LBS marketplace in the United States will generate $ 4- 8 billion annually by 2005.

4 2- LBS Applications: For public services: People finder ( either after emergency center calls or by automatic tracking). Assets tracking. Entertainment & Tourism. For Government sectors and enterprises: City administration, intelligent traffic management. Fleet management ( monitor, control and steer of their crews movement). Safety of the police officer – ability to locate officer in an emergency situation and react accordingly. Resource (person or vehicle) management. For wireless network planning: Network optimization planning (achieving efficient and effective resource allocation).

5 3- Location Estimation Techniques (LET) Factors Influencing Accuracy : Wireless Signal Propagation NOLS (Reflection, Diffraction, Scattering ). Multiple access interference. Multipath fade. Noise. Coverage Areas Urban, suburban and rural areas. Indoor, outdoor. Location Estimation Approach. Geometric Based on triangulation calculations, very sensitive to wireless signal propagation variations. Statistics Based on statistical and probability calculations, affected by wireless signal propagation variations. Artificial intelligence Based on learning capability, robust to wireless signal propagation variations.

6 Location Estimation Measurement Methods. Network- Based, handset- based, and hybrid

7 4 -What is the proposal Technique? it will be a real time location estimation technique based on neural networks (NN). It will be applied to TETRA network. Wireless radio signal parameters that can be extracted from the network such as RSSI, TDOA,,... etc and cell-ID. These parameters were not used together before as NN input vector. Also, a decision criterion such as LS or MLH will be used to resolve the ambiguous coverage areas. All available data will be fed in NN to locate TETRA Mobile Radio.

8 (Continued) The proposed technique LBS Server GIS or Mapping Application TETRA Network TETRA Gateway

9 5-Why TETRA is selected as a platform for the application of the proposed technique? 1- TETRA is the only open standard wireless trunking system that is manufactured by more than one company. 2- It is the preferable trunking system for safety and security sectors. So, its implementation is spreading widely( 85% yearly increase,2 sys. in S.A). 3- So far, most of the safety and security sectors used AGPS to estimate their fleet locations. Therefore, independent source for LBS is highly needed. 4- Cell radius for TETRA is around 45km, while for GSM is around 10km.So, locating of its MR need more robust technique which need more investigation. 5- TETRA Radios( base station, handheld, or vehicle) have power transmission classes. But GSM mobile station transmits with one power class. 6- TETRA wireless signal model is different than GSM wireless signal model ( see the table below).

10 Comparison Table Between GSM and TETRA Systems NO Features GSM TETRA 1Modulation GMSK 2Min. SIR 9dB 19dB 3Receiver sensitivities RX: 1) Static : Base Station: Mobile Station: 2) Dynamic: Base Station: Mobile Station: -113 dBm -112 dBm -104 dBm -103 dBm -115 dBm -112 dBm -106 dBm -103 dBm 4Propagation Model ETSI EN 300 910 V7.3.1ETSI EN 300 392-2 V2.4.2 5Cell radius10Km45Km

11 6Handover measurements (RxLev) &(RxQual) down Link Every 0.5 sec. On network 1ms or 4ms On mobile station 7Frequency Band900 MHz 400 MHz 8Bandwidth for one channel25kHz (full rate) 12.5kHz(half rate) 8 slots 6.25kHz 4 slots 9Call set-up times< 10 sec(1-3s)< 1 sec(300ms) (continued) Comparison Table Between GSM and TETRA Systems

12 Previous studies : All previous studies have been proposed for Public cellular networks such as GSM and UMTS(3G). There is no published available for LBS on TETRA network. Location Estimation Approach. 1- Geometric Based on triangulation calculations, very sensitive to wireless signal propagation variations. 2- Statistics Based on statistical and probability calculations, affected by wireless signal propagation variations. MLH and LS have been used also sectoring of cell converge have been adopted. 3- Artificial intelligence Based on learning capability, robust to wireless signal propagation variations. The parameters that have been measured to locate MS and fed into NN were TDOR, AOA and TOA. But, RSSI and time-based measurements have never been used together in NN.

13 Research Objectives : The main goal of the proposed work is to develop a technique for improving the accuracy of TETRA Mobile Radios locations estimation compared with the existing TETRA positioning techniques. Specifically, the proposed research is seeking to achieve the following objectives: 1- Developing a simulation package for TETRA wireless network that is applicable in urban environment (e.g., Riyadh City). 2- Identifying the main TETRA signal parameters that can be extracted for building TETRA radio location estimator. 3- Developing a model based on artificial intelligence for radio location estimation. 4- Validating the proposed estimator technique using simulated and real data. 5- Comparing the effectiveness of the proposed location estimation method with that of existing TETRA positioning techniques.

14 Research Methodology : The study in this research will be both theoretical and experimental. The theoretical part will include: 1- Mathematical derivation of signal model and MS location estimator. 2- Computer simulation will be used to better understand the proposed technique. 3- Identify TETRA main signal parameters that are of potential value for location estimation. The experimental part of this work will include: 1- Field measurements conducted in Riyadh City to validate the proposed location estimation method. 2- Evaluate its performance against other existing TETRA estimation methods.

15 Sarfaraz Khokhar, "Technology Trends: Choose wisely, Which Location Estimation Technologies Meet Market Needs?" (http://www.geoplace.com/bg/2001/0401/0401tt.asp).http://www.geoplace.com/bg/2001/0401/0401tt.asp 1.Federal Communications Commission (FCC) website (http://www.fcc.gov.). 2.Jim McGeough, "Wireless Location Positioning based on Signal Propagation Data" ( http://www.wirelessdevnet.com/library/geomode1.pdf).http://www.wirelessdevnet.com/library/geomode1.pdf 3.SenseStream company website introduction (http://www.sensestream.com/snapware_lbaf.php).http://www.sensestream.com/snapware_lbaf.php 4.Maurizio A. Spirito, "On the Accuracy of Cellular Mobile Station Location Estimation", IEEE Transactions on Vehicular Technology, Vol. 50, No. 3, pp. 674 – 685, May 2001. 5.Isaac K. Adusei, K.Kyamakya, and Klaus Jobmann, "Mobile Positioning Technologies in Cellular Networks: An Evaluation of their Performance Metrics", Proceeding of the IEEE MILCOM Conference, Vol.2, pp. 1239 – 1244, Oct.2002. 6.Michael McGuire, Konstantinos N. Plataniotis, and Anastasios N. Venetsanopoulos, "Location of Mobile Terminals Using Time Measurements and Survey Points", IEEE Transactions on Vehicular Technology, Vol. 52, No. 4, pp. 999 – 1011, July 2003. 7.Shohei Kikuchi, Akira Sano, Hiroyuki Tsuji, and Ryu Miura, "A Novel Approach to Mobile-Terminal Positioning Using Single Array Antenna in Urban Environments", Proceeding of the IEEE Vehicular Technology Conference (VTC 2003), Vol. 2, pp. 1010 – 1014, Oct.2003. 8.K.W. Cheung, H.C.So, W.-K.Ma, and Y.T. Chan, "Least Squares Algorithms for Time-Of-Arrival- Based Mobile Location", IEEE Transactions on Signal Processing, Vol. 52, No. 4, pp. 1121 – 1128, April 2004. References :

16 10.Teemu Roos, Petri Myllymaki, and Henry Tirri, "A Statistical Modeling Approach to Location Estimation", IEEE Transactions on Mobile Computing, Vol. 1, No. 1, pp. 59-69, Quarter 1, 2002. 11.Masato ASO, Takahiko SAIKAWA and Takeshi HATTORI, "Mobile Station Location Estimation Using the Maximum Likelihood Method in Sector Cell Systems" Proceeding of the IEEE Vehicular Technology Conference(VTC 2002), Vol. 2, pp. 1192 – 1196, Sept.2002. 12.Peter J. Voltz and David Hernandez, "Maximum Likelihood Time of Arrival Estimation for Real-Time Physical Location Tracking of 802.11a/g Mobile Stations in Indoor Environments", Proceeding of the IEEE Position Location and Navigation Symposium( PLANS 2004 ), pp. 585 – 591, April 2004. 13.Simon Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed., Prentice Hall, upper saddle river, New Jersey, USA, 1999. 14.Roberto Battiti,Thang Le Nhat and Alessandro Villani, Location-aware computing: A neural network model for determining location in wireless LANS, University of Trento, Technical Report,No.DIT-02- 0083,February2002.( http://eprints.biblio.unitn.it/archive/00000233/01/83.pdf) 15.H. Zamiri-Jafarian, M. M. Mirsalehi, I. Ahadi-Akhlaghi and H. Keshavarz, A Neural Network-based Mobile Positioning with Hierarchical Structure, Proceeding of the IEEE Vehicular Technology Conference(VTC 2003), Vol. 3, pp.2003-2007, April 2003. 16.Shiang-Chun Liou and Hsuan- Chia Lu, Applied Neural for Location Prediction and Resource Reservation Scheme in Wireless Networks, Proceeding of the IEEE International Conference on Communication Technology(ICCT 2003),Vol. 2,pp.958-961,April 2003. 17.Sandrine Merigeault, Mickael Batariere, and Jean Noel Patillon "Data Fusion Based on Neural Network for the Mobile Subscriber Location", Proceeding of the IEEE Vehicular Technology Conference (VTC 2000), Vol. 2, pp. 536 – 541, Sept. 2000. 18.John Dunlop, Demesis Girma, and James Irvine, Digital Mobile Communications and the TETRA System, John Wiley & Sons, Baffins lane, Chichester, West Sussex, England, 1999. 19.Locus Portal company, Locus TETRA Location system, website (http://www.locusportal.com.).http://www.locusportal.com 20.Peter Clemons, "TETRA Contracts up 84 Percent", an article on Radio Resource International Journal, Quarter 2, page 8, 2004.


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