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Fuzzy technology application for mobile positioning in cellular communication

By Aleksandar Stojcevski

Abstract

In engineering systems there is generally two classes of knowledge: objective knowledge,\ud which can be quantified using the laws of traditional mathematics, and subjective or\ud intelligent knowledge, that cannot be modeled mathematically but can be expressed in\ud linguistical terms. Fuzzy Logic (FL) is a method that combines these two forms of\ud knowledge, and as such provides a powerful tool for solving real engineering problems.\ud A fuzzy logic system (FLS) is the methodology of applying FL to engineering systems.\ud In general, a FLS can be considered as a non-linear mapping of crisp (firm) input data to\ud crisp output data. It is the inclusion of subjective knowledge in a FLS that leads to a\ud plethora of mapping possibilities, which may not be possible using traditional\ud mathematical modeling techniques. A fuzzy logic system consists of four main elements:\ud fuzzification, rule based, inference engine and deffuzification.\ud Fuzzy logic has been successfully adopted in many real-world automatic control systems\ud including automobile transmission, subway systems, industrial robots, washing machines,\ud cameras and air-conditioners. In contrast, the utilization of fuzzy logic in mobile\ud communications systems is recent and limited. Understanding general mobile\ud communications is essential in order to go on and develop a mobile positioning\ud application.\ud The successful applications of Fuzzy Logic Control (FLC) techniques in many areas draw\ud a huge amount of attention to its industrial applications. However, lack of structured\ud methods and tools for design and analysis is preventing this revolutionary controller from\ud playing a more significant role in mobile communications.\ud A methodology to construct and analyse a FL controller to be used in mobile positioning\ud would significantly improve the efficiency of FLC design, increase the quality of FLC by\ud allowing the designer to develop and design the controller based on some specifications\ud and requirements, and then validate that design

Topics: 1005 Communications Technologies, 1204 Engineering Design, School of Engineering and Science, Mobile communication systems, Mobile radio stations, Automatic control systems, Cellular telephones, Fuzzy logic
Year: 2000
OAI identifier: oai:eprints.vu.edu.au:18213

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Citations

  1. (1996). [2] Federal Communications Commission, "Reports and Order and Further Notice of Proposed Rulemaking, CC Docket No.94.102, Revision of the Commission's Rules to Ensure Compatibility with Enhanced Emergency Calling Systems",
  2. (1995). A Locally Optimal Handover Algorithm",
  3. (1993). A Method for Analyzing Handover Algorithms",
  4. (1997). A new path-gain/delay-spread propagation model for digital celular channels",
  5. (1991). A physical radio channel model",
  6. (1990). Advanced Least square applied to position-fixing,
  7. (1993). An Introduction to Fuzzy Control", Springer-Verlag
  8. (1994). An Urban Positioning Approach Applying Differential Methods to commercial FM Radio Emissions for Ground Mobile Use",
  9. (1991). Analysis of Handover Algorithms",
  10. (1998). Application of Fuzzy Logic to Handover Control in Cellular Mobile Communications Networks", Fuzzy Systems Design, Social and Engineering Applications, Physica-Verlag,
  11. Applications of Fuzzy Algorithms for Control of Simple Dynamic Plant",
  12. (1974). Artificial Intelligence Techniques in Control of Real Dynamic Systems", PhD-thesis,
  13. (1994). Automatic Vehicle Location in Cellular Communications Systems",
  14. Comparison of Different Handover Strategies for High Capacity Cellular Mobile Radio Systems",
  15. (1986). Crosscorrelation between the envelope of 900 MHz signals received at a mobile radio base station site",
  16. (1994). Delay spread measurements for digital cellular channel in Toronto",
  17. (1983). Derivation of Fuzzy Control Rules from Human Operator's Control Action",
  18. (1994). Direction Based Handover Algorithm for Urban Macrocells",
  19. (1996). Direction-of-arrival of partial waves in wideband mobile radio channels for intelligent antenna concepts",
  20. (1973). Effects on correlation between two mobile radio base station antennas",
  21. Empirical Formula for Propagation Loss in Land Mobile Radio Service",
  22. (1990). Evaluation of a Proposed Handover Algorithm for the GSM Cellular Systems",
  23. (1997). Evaluation of Positioning Measurement System",
  24. (1998). Evaluation of Positioning Measurements System",
  25. (1998). Final Positioning channel model",
  26. (1998). Final Positioning Channel Model", Ericsson Document,
  27. (1994). Final propagation model", R2020/TDE/PS/DS/P/040/bl,
  28. (1993). Foundations of Mobile Radio Engineering",
  29. (1995). Fuzzy Logic Adaptive Handoff Algorithm",
  30. (1990). Fuzzy Logic in Control Systems, Fuzzy Logic Controller-Part II",
  31. Fuzzy Sets",
  32. (1990). Handover Criteria for City Micro Cellular Radio System",
  33. Handover Criterion for Macro Cellular Systems",
  34. (1992). Handover in Microcellular Based Personal Telephony Systems",
  35. (1990). Handover Initiation and Control for Highway Microcells", Fourth Nordic Seminar Mobile Radio Communications",
  36. Handover Strategies in Microcellular Systems",
  37. (1993). Impulse response measurements in the 902-928 and 1850-1990 MHz bands in macrocellular environments",
  38. (1989). Intelligent Identification and Control for Autonomous Guided Vehicles Using Adaptive Fuzzy-Based Algorithms",
  39. (1992). Mobile Radio Communications",
  40. (1998). Multipath Channel Behaviour Characterization Data",
  41. (1992). Navigation and Positioning Systems and Methods Using Uncoordinated Beacon Signals", United States Patent,
  42. (1991). On the System Design Aspect of CDMA Applied to Digital Cellular and PCN",
  43. (1994). Performance Analysis of Mobile Positioning Using Existing CDMA Network",
  44. Phase Plane Analysis Tools for a Class of Fuzzy Control Systems",
  45. (1997). Power azimuth spectrum in outdoor environments",
  46. (1997). Power-delay profile of spartial channel model",
  47. (1998). Proposal for Common System Simulator",
  48. (1999). Results from Medium scale trail of an E-OTD system in an urban environment, CPS,
  49. Stojcevski SampRate = 16; samples/bit) Fs = SampRate*(13e6/48); % internal sampling rate (no. of % SampRate times bit rate MonteCarloRuns = 300; %=_=___=_____== propagation model parameters ChannelType = 'BadUrban' DiffuseDelay= 15e-6; lambda
  50. (1999). System performance evaluation of mobile positioning methods",
  51. (1999). The use of a weighted RMS function to reliably represent the distribution to location measurements" Cambridge Positioning Systems,
  52. (1998). Time of arrival estimation of narrow-band TDMA signals for mobile positioning",
  53. (1999). Trends in Cellular Position Location Techniques-An Overview",
  54. UHF band spatial correlation characteristics of mobile base station antennas",
  55. (1998). Wideband channel measurements in central Stockholm",

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