Cellular Positioning

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Cellular Positioning

• Shashika BiyanwilaResearch Engineer

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Outline

• What is Cellular Positioning
• Positioning Parameters
• Feasible Approaches Identified
• Implementation
• Some Trial Results
• Future Approach of the Research

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What is cellular positioning ?

• Determining the position of a Mobile Station
  (MS), using location sensitive parameters
• Why ????
• To provide Location Based Services

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Applications of cellular positioning

• Operator services
• Billing
• Network management
• Location based services
• Wireless Gaming

• Assistance
• Roadside assistance
• Personal or vehicle emergency
• Alarm management
• Driving Directions

• Tracking
• Tracking criminals
• Tracking external resources containers etc

• Monitoring
• Monitoring delivery process
• Fleet freight tracking
• Personal Child Security
• Mobile Worker management

• Information
• Traffic
• Nearest service
• news
• navigation help
• advertising
• Information Directory

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Positioning Parameters

• Cell-ID
• Received Signal Strength Intensity (RSSI)
• Timing Advance (TA)
• Uplink Time (Difference) Of Arrival (TDOA)
• Downlink Observed Time Differences (E-OTD)
• Angle of Arrival (AOA)

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Feasible Approaches Identified
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1. Geometrical Approach

• Based on distance measurements
• Two Steps
• - Distance calculation
• - Location Estimation

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Geometrical approach contd..

• Distance Calculation
• - Measure the RSSI from neighboring cells
• - Apply Propagation models to calculate the
  distance
• Propagation Models
• - Hata Model
• - Extended Hata Model
• - Lees Model
• - CCIR Model
• - Walfisch-Ikegami Model (for micro cells)

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2. Statistical Approach

• Construct a statistical propagation model for the
  RSSI
• Find RSSI at distance d from the transmitter
• Offsite calibration is necessary to estimate the
  propagation parameters
• Define a probability distribution for the RSSI
• Location estimation problem is solved as an
  inverse or, rather, inference problem

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Statistical Approach contd..

• Log-loss or Log-distance model
• Gaussian Probability Distribution
• Propagation Parameter Estimation
• - Maximum Likelihood estimation
• Location Estimation
• - Maximum A posteriori Probability

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Statistical Approach cntd..

• Area being considered is divided into several
  squares
• A posterior probability of the location be within
  a square, is calculated for each square

Square with Maximum A posterior Probability
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3. Database correlation Method (DCM)

• Involves a database of reference fingerprints for
  the whole area of interest.
• Fingerprint a recorded measurement sample from
  a certain location in the area
• ?GPS coordinates of a location
• ?RSSI (from available cells) in that location

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DCM contd

• How to collect fingerprints?
• By measurements
• Using a Network planning tool
• High sampling resolution is needed.

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DCM contd

• Location estimation

• Compare the input measurement with reference
  fingerprints
• - Using Cost Functions
• Location of the best matching reference
  fingerprint
• ?Estimated Location

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Implementation
Hardware Environment
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Trial Results

• Urban
• - Wellawaththa to Kolpetty
• Suburban
• - Katubedda to Piliyandala
• Rural
• - Ibbagamuwa

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• Urban area..

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• Suburban area .

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Rural area ..
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Future Approach of the Research

• Improvements to the current DCM approach
• Drawbacks
• - Few instances of poor estimations
• - Creating, updating maintenance of the
  database
• How To Overcome
• - Refined estimation techniques
• - Use of a Network planning tool to create
  fingerprints

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• Implementation of a positioning engine and
  associated services
• Services
• Get your own location
• Track others web-based location on a map

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Thank You