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`PROVISIONAL
`APPLICATION
`NUMBER
`
`SERIAL.NUMBER
`60/056,590
`F'F:OV I HI (JI\I(.:j!....
`
`FILING DATE CLASS
`08/20/97
`
`SUBCLASS
`
`~DENNIS JAY DUF'RAY. DENVER,
`
`CO; CHARLES KARR,
`
`i
`
`**CONTINUING DATA*********************
`VEF~ I F. I E:J>
`
`**FOREIGN/PCT APPLICATIONS************
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`Verified and
`DENNIS J. DUPRAY
`
`I 222 so. MARION PARKWAY
`I I>ENVEH co ::::020'3
`
`WIRELESS LOCATION USING MUL ·.PLE SIMULTANEOUS LbCATION ESTIMATORS
`
`U.S. DEPT. OF COMMJ PAT. & TM-Pf0.438L (Rev.12·94
`
`-
`
`Form PT0-1625
`
`?\ , , f ,{\
`tRev. 5195) ~ r, ~ ~ f; \.lJ lJ'.I(.l.)
`.~J<l-JuL ~
`
`(FACE)
`
`Apple, Inc. Exhibit 1043 Page 1
`
`

`
`I' \II
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`
`,.
`I 1 '' .
`I. 6,0 fOS6590
`1\\\11\\\\\\\\\\\\\\11\\\\\\\\\\1\\11\\\
`U0(2U/9 1
`
`Date
`Entered
`or
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`PATENT APPLICATION
`111111111111111111~111111111111111111111111111111
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`60056590
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`CONTENTS
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`APPROVED FOR LlOENSE 0 .
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`0{',?-
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`Date~·· l
`Received
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`papers.
`----~--~----- 1. Application
`2. f<v-e;:t<~f- ,·frrr- ~ --
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`________ 4. ___________________ - - - - - - - - - -
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`~------7.------------------- ----------
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`__ __ 15. - - - - - - - - - - - - - - - - - (cid:173)
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`___ _ 16. - - - - - - - - : - - - - - - - - - - - (cid:173)
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`(FRONT)
`
`Apple, Inc. Exhibit 1043 Page 2
`
`

`
`POSITION
`
`CLASSIFIER
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`1 nez5<9-3 I
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`(LEFT INSIDE)
`
`Apple, Inc. Exhibit 1043 Page 3
`
`

`
`62101 U.S. PTO
`
`1111111 ~11111111111111111111111111111
`08/20/97
`
`·--
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`:1
`PROVISIONAL APPLICAT/ONfCOVER SHEET ~ff/#~
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`This is a request for :filing a PROVISIONAL APPLICATION FOR PATENT under 37 CFR 1.53(b)(2).
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`DUPRAY
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`DENNIS
`CHARLES
`
`JAY
`
`222 So. Marion Parkway, Denver, Colorado 80209
`400 Sandbrook Lane, Tuscaloosa, Alabama 35405
`
`TITLE OF THE INVENTION (280 characters max)
`
`"WIRELESS LOCATION USING MULTIPLE SIMULTAENOUS LOCATION ESTIMATORS"
`
`CORRESPONDENCE ADDRESS
`
`Dennis J. Dupray
`222 So. Marion Parkway
`Denver
`
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`
`STATE I Colorado
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`i.l Specification
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`
`Apple, Inc. Exhibit 1043 Page 4
`
`

`
`Property of lntellabs LLC: Confidential
`
`WIRELESS LOCATION USING MULTIPLE
`SIMULTANEOUS LOCATION ESTIMATORS
`
`page 1 of 173
`
`Apple, Inc. Exhibit 1043 Page 5
`
`

`
`Property of Intellabs LLC: Confidential
`
`Table of Contents
`
`1. FIELD OF THE INVENTION
`
`2. BACKGROUND OF THE INVENTION
`
`2.1 Introduction
`
`2.2 Radio Propagation Background
`2. 2.1 RF Propagation in Free Space
`2.2.2 Environmental Clutter and RF Propagation Effects
`2.2.3 Relating Received Signal Strength to Location
`2.2.4 Summary of Factors Affecting RF Propagation
`
`3. SUMMARY OF THE INVENTION
`
`3.1 OBJECTS OF THE INVENTION
`
`3.2 DEFINITIONS
`
`3.3 SUMMARY DISCUSSION
`
`4. BRIEF DESCRIPTION OF THE DRAWINGS
`
`5. DETAILED DESCRIPTION
`
`5.1 Detailed Description Introduction
`
`5.2 Location Center - Network Elements API Description
`
`5.3 LOCATION CENTER IDGH LEVEL FUNCTIONALITY
`
`5.4 Location Hypothesis Data Representation
`
`5.5 Coverage Area: Area Types And Their Determination
`
`5.6 Location Information Data Bases And Data
`5.6.1 Location Data Bases Introduction
`5.6.2 Data Representations for the Location Signature Data Base
`
`5. 7 Location Center Architecture
`5. 7.1 Overview of Location Center Functional Components
`5. 7 .1.1 Low Level Wireless Signal Processing Subsystem for Receiving and Conditioning Wireless
`Signal Measurements
`5.7.1.2 Initial Location Estimators: First Order Models
`5. 7 .1. 3 Evaluator for Location Hypotheses: Hypothesis Evaluator
`5. 7 .1. 3 .1 An Introduction to the Context Aduster
`5.7.1.3.2 An Introduction to the Location Hypothesis Analyzer
`5.7.1.3.3 The MS Status Repository
`5.7.1.3.4 An Introduction to the Location Estimator
`
`page 2 of 173
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`Apple, Inc. Exhibit 1043 Page 6
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`

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`Property of Intellabs LLC: Confidential
`
`5.7.1.4 Control and Output Gating Modules
`5. 7 .1.5 System Tuning and Adaptation: The Adaptation Engine
`
`5.8 Implementations of First Order Models
`5.8.1 Distance First Order Models (TOAITDOA)
`5.8.2 Coverage Area First Order Model
`5.8.3 Location Base Station First Order Model
`5.8.4 Stochastic First Order Model
`5.8.5 Pattern Recognition and Adaptive First Order Models
`5.8.5.1 Statistically Based Pattern Recognition First Order Models
`5.8.5.2 Adaptive!Trainable First Order Models
`5.8.5.3 Adaptive!Trainable First Order Models
`5.8.6 Artificial Neural Networks ForMS Location
`5.8.6.1 Artificial Neural Networks That Converge on Near Optimal Solutions
`
`5.9 Artificial Neural Networks as MS Location Estimators for First Order Models
`5. 9.1 Artificial Neural Network Input and Output
`5.9.2 Artificial Neural Network Training
`5.9.3 Finding Near-Optimal Location Estimating Artificial Neural Networks
`5.9.4 Locating a Mobile Station Using Artificial Neural Networks
`
`5.10 LOCATION SIGNATURE DATA BASE
`5.10.1 LOCATION SIGNATURE PROGRAM DESCRIPTIONS
`5.1 0.1.1 Update Location signature Database Program
`5.10.1.2 Confidence Aging Program
`5.10.1.3 Confidence Enhancement Program
`5.10 .1.4 Location Hypotheses Consistency Program
`5.10.1.5 Location Signature Comparison Program
`5.10.1.6 Computed Location Signature Program
`5.10.1. 7 Geographic Area Representation Program
`5.10.1. 8 Location signature Comparison Program
`
`5.11 DETAILED DESCRIPTION OF THE HYPOTHESIS EVALUATOR
`5.11.1 Hypothesis Evaluator Introduction
`5.11.2 Hypothesis Evaluator High Level Components
`5.11.2.1 Location Hypothesis Analyzer Further Details
`5.11.2.2 Individual Hypothesis Evaluator Components
`5.11.2.3 Context Adjuster Embodiments
`5.11.3 HYPOTHESIS ANALYZER
`5.11.4 MS STATUS REPOSITORY
`5.11.5 LOCATION ESTIMATOR
`5.11.6 DETAILED DESCRIPTION OF HYPOTHESIS ANALYZER COMPONENTS
`5.11.6.1.1 Analytical Reasoner
`5.11.6.1.2 Modules of an Embodiment of the Analytical Reasoner.
`
`5.12 Mobile Base Station Location Subsystem Description
`5.12.1 Mobile Base Station subsystem Introduction
`5.12.2 MBS Subsystem Architecture
`5.12.3 MBS Data Structure Remarks
`5.12.4 MBS Location Estimating Strategy
`
`6. APPENDIX A: MBS FUNCTION EMBODIMENTS
`
`page 3 of 173
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`Apple, Inc. Exhibit 1043 Page 7
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`

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`Property of Intellabs LLC: Confidential
`
`6.1 Mobile Base Station Controller Program
`
`6.2 Lower Level MBS Function Descriptions
`
`7. APPENDIX B: PSEUDO CODE FOR A GENETIC ALGORITHM
`
`8. APPENDIX C: LOCATION DATABASE MAINTENANCE PROGRAMS
`
`9. APPENDIX D: CONTEXT ADJUSTER EMBODIMENTS
`
`9.1 A description of the high level functions in a first embodiment of the Context Adjuster
`
`9.2 A Second Embodiment of the Context Adjuster.
`
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`10. APPENDIX E: HISTORICAL DATA CONFIDENCE ADJUSTER PROGRAM 163
`
`11. PATENT CLAIMS
`
`12. 1.ABSTRACT
`
`165
`
`173
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`page 4 of 173
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`Apple, Inc. Exhibit 1043 Page 8
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`

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`Property of Intellabs LLC: Confidential
`
`LOCATION OF A MOBILE STATION USING A COMMERCIAL
`WIRELESS INFRASTRUCTURE
`
`1. FIELD OF THE INVENTION
`
`The present invention is directed generally to a system and method for locating people
`or objects, and in particular to a system and method for locating a wireless mobile radio
`station.
`
`2. BACKGROUND OF THE INVENTION
`
`2.1 Introduction
`Wireless communications systems are becoming increasingly important worldwide.
`Wireless cellular telecommunications systems are rapidly replacing conventional wire-based
`telecommunications systems in many applications. Cellular radio telephone networks
`("CRT"), and specialized mobile radio and mobile data radio networks are examples. The
`general principles of wireless cellular telephony have been described variously, for example in
`U. S. Patent 5,295, 180 to Vendetti, et al, which is incorporated herein by reference.
`There is great interest in using existing infrastructures for wireless communication
`systems for locating people and/or objects in a cost effective manner. Such a capability
`would be invaluable in a variety of situations, especially in emergency or crime situations.
`Due to the substantial benefits of such a location system, several attempts have been made to
`design and implement such a system.
`Systems have been proposed that rely upon signal strength and trilateralization
`techniques to permit location include those disclosed in U.S. Patents 4,818,998 and
`4,908,629 to Apsell et al. ("the Apsell patents") and 4,891,650 to Sheffer ("the Sheffer
`patent"). The Apsell patents disclose a system employing a "homing-in" scheme using radio
`signal strength, wherein the scheme detects radio signal strength transmitted from an
`unknown location. This signal strength is detected by nearby tracking vehicles, such as police
`cruisers using receivers with directional antennas. Alternatively, the Sheffer patent discloses
`a system using the FM anal6"g cellular network. This system includes a mobile transmitter
`located on a vehicle to be located. The transmitter transmits an alarm signal upon activation
`to detectors located at base stations of the cellular network. These detectors receive the
`transmitted signal and transmit, to a central station, data indicating the signal strength of the
`received signal and the identity of the base stations receiving the signal. This data is
`processed to determine the distance between the vehicle and each of the base stations and,
`through trilateralization, the vehicle's position. However, these systems have drawbacks that
`include high expense in that special purpose electronics are required. Furthermore, the
`systems are generally only effective in line-of-sight conditions, such as rural settings. Radio
`wave surface reflections, refractions and ground clutter cause significant distortion, in
`determining the location of a signal source in most geographical areas that are more than
`sparsely populated. Moreover, these drawbacks are particularly exacerbated in dense urban
`
`page 5 of 173
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`Apple, Inc. Exhibit 1043 Page 9
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`

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`Property of Intellabs LLC: Confidential
`
`canyon (city) areas, where errors and/or conflicts in location measurements can result in
`substantial inaccuracies.
`Another example of a location system using time of arrival and triangulation for
`location are satellite-based systems, such as the military and commercial versions of the
`Global Positioning Satellite system ("GPS"). GPS can provide accurate position
`determination (i.e., about 100 meters error for the commercial version ofGPS) from a time(cid:173)
`based signal received simultaneously from at least three satellites. A ground-based GPS
`receiver at or near the object to be located determines the difference between the time at
`which each satellite transmits a time signal and the time at which the signal is received and,
`based on the time differentials, determines the object's location. However, the GPS is
`impractical in many applications. The signal power levels from the satellites are low and the
`GPS receiver requires a clear, line-of-sight path to at least three satellites above a horizon of
`about 60 degrees for effective operation. Accordingly, inclement weather conditions, such as
`clouds, terrain features, such as hills and trees, and buildings restrict the ability of the GPS
`receiver to determine its position. Furthermore, the initial GPS signal detection process for a
`GPS receiver is relatively long (i.e., several minutes) for determining the receiver's position.
`Such delays are unacceptable in many applications such as, for example, emergency response
`and vehicle tracking.
`Differential GPS, or DGPS systems offer correction schemes to account for time
`synchronization drift. Such correction schemes include the transmission of correction signals
`over a two-way radio link or broadcast via FM radio station subcarriers. These systems have
`been found to be awkward and have met with limited success.
`Additionally, GPS-based location systems have been attempted in which the received
`GPS signals are transmitted to a central data center for performing location calculations.
`Such systems have also met with limited success due, for example, to the limited reception of
`the satellite signals and the added expense and complexity of the electronics required for an
`inexpensive location mobile station or handset for detecting and receiving the GPS signals
`from the satellites.
`
`2.2 Radio Propagation Background
`The behavior of a mobile radio signal in the general environment is unique and
`complicated. Efforts to perform correlations between radio signals and distance between a
`base station and a mobile station are similarly complex. Repeated attempts to solve this
`problem in the past have been met with only marginal success. Factors include terrain
`undulations, fixed and variable clutter, atmospheric conditions, internal radio characteristics
`of cellular and PCS systems, such as frequencies, antenna configurations, modulation
`schemes, diversity methods, and the physical geometries of direct, refracted and reflected
`waves between the base stations and the mobile. Noise, such as man-made externally sources
`(e.g., auto ignitions) and radio system co-channel and adjacent channel interference also
`affect radio reception and related performance measurements, such as the analog carrier-to(cid:173)
`interference ratio (CII), or digital energy-per-bit/Noise density ratio (Eb!No) and are particular
`to various points in time and space domains.
`
`page 6 of 173
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`Apple, Inc. Exhibit 1043 Page 10
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`

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`Property of Intellabs LLC: Confidential
`
`2.2.1 RF Propagation in Free Space
`
`Before discussing real world correlations between signals and distance, it is useful to
`review the theoretical premise, that of radio energy path loss across a pure isotropic vacuum
`propagation channel, and its dependencies within and among various communications channel
`types.Fig. BG-1 illustrates a definition of channel types arising in communications:
`Over the last forty years various mathematical expressions have been developed to assist the
`radio mobile cell designer in establishing the proper balance between base station capital
`investment and the quality of the radio link typically using radio energy field-strength, usually
`measured in microvolts/meter, or decibels.
`First consider Hata' s single ray model. A simplified radio channel can be described as:
`
`(Equation 1)
`
`where Gi = system gain in decibels
`Lp= free space path loss in dB,
`F= fade margin in dB,
`L1= transmission line loss from coaxials used to connect radio to antenna, in dB,
`Lm= miscellaneous losses such as minor antenna misalignment, coaxial corrosion,
`increase in the receiver noise figure due to aging, in dB,
`Lb= branching loss due to filter and circulator used to combine or split transmitter and
`receiver signals in a single antenna
`Gt= gain of transmitting antenna
`Gr= gain of receiving antenna
`
`Free space path loss1 Lp as discussed in Mobile Communications Design Fundamentals, William C.
`Y. Lee, 2nd, Ed across the propagation channel is a function of distance d, frequency
`/(for fvalues < 1 GHz, such as the 890-950 mHz cellular band):
`
`(equation 2)
`
`received power in free space
`where P or
`P t = transmitting power
`c =speed of light,
`
`The difference between two received signal powers in free space,
`
`2 J = (20) log(!!LJ(dB)
`J1 P = (10)1og(P or
`d2
`por1
`
`(equation 3)
`
`page 7 of 173
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`Apple, Inc. Exhibit 1043 Page 11
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`

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`Property of Intellabs LLC: Confidential
`
`indicates that the free propagation path loss is 20 dB per decade. Frequencies between 1 GHz
`and 2GHz experience increased values in the exponent, ranging from 2 to 4, or 20 to 40
`dB/decade, which would be predicted for the new PCS 1.8- 1.9 GHz band.
`
`This suggests that the free propagation path loss is 20 dB per decade. However, frequencies
`between 1 GHz and 2 GHz experience increased values in the exponent, ranging from 2 to 4,
`or 20 to 40 dB/decade, which would be predicted for the new PCS 1.8- 1.9 GHz band. One
`consequence from a location perspective is that the effective range of values for higher
`exponents is an increased at higher frequencies, thus providing improved granularity of
`ranging correlation.
`
`2.2.2 Environmental Clutter and RF Propagation Effects
`
`Actual data collected in real-world environments uncovered huge variations with
`respect to the free space path loss equation, giving rise to the creation of many empirical
`formulas for radio signal coverage prediction. Clutter, either fixed or stationary in geometric
`relation to the propagation of the radio signals, causes a shadow effect of blocking that
`perturbs the free space loss effect. Perhaps the best known model set that characterizes the
`average path loss is Hata' s, "Empirical Formula for Propagation Loss in Land Mobile Radio",
`M. Hata, IEEE Transactions VT-29, pp. 317-325, August 1980, three pathless models, based
`on Okumura's measurements in and around Tokyo, "Field Strength and its Variability in VHF
`and UHF Land Mobile Service", Y. Okumura, et al, Review of the Electrical
`Communications laboratory, Vol16, pp 825-873, Sept.- Oct. 1968.
`
`The typical urban Hata model for Lp was defined as Lp = Lhu:
`
`LHu = 69.55+ 26.16log(f) -13.82log(hss )- a(hMS )+ ((44.9 6.55log(Hss) log( d)[ dB])
`(Equation 4)
`
`where LHu = path loss, Hata urban
`h.ss base station antenna height
`hMs= mobile station antenna height
`d =distance BS-MS in km
`a(hMS) is a correction factor for small and medium sized cities, found to be:
`
`a(hMs) is a correction factor for small and medium sized cities, found to be:
`
`page 8 of 173
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`Apple, Inc. Exhibit 1043 Page 12
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`

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`Property of lntellabs LLC: Confidential
`
`1log(f 0. 7)hMS - 1.56log{f 0.8) = a(hMS)
`
`For large cities the correction factor was found to be:
`
`2
`a(hM8) = 3.2 [log11.75hMS] -4.97
`
`assuming f is equal to or greater than 400 mHz.
`
`The typical suburban model correction was found to be:
`
`(Equation 5)
`
`(Equation 6)
`
`(Equation 7)
`
`The typical rural model modified the urban formula differently, as seen below:
`
`2
`LHrural = LHu-4.78 (logf) + 18.33logf-40.94 [dB]
`
`(Equation 8)
`
`'"
`
`Although the Hata model was found to be useful for generalized RF wave prediction
`in frequencies under 1 GHz in certain suburban and rural settings, as either the frequency
`and/or clutter increased, predictability decreased. In current practice, however, field
`technicians often have to make a guess for dense urban an suburban areas (applying whatever
`model seems best), then installing a base stations and begin taking manual measurements.
`Coverage problems can take up to a year to resolve.
`
`2.2.3 Relating Received Signal Strength to Location
`
`Having previously established a relationship between d and P or, reference equation 2
`above: d represents the distance between the mobile station (MS) and the base station (BS);
`Por represents the received power in free space) for a given set of unchanging environmental
`conditions, it may be possible to dynamically measure P or and then determine d.
`In 1991, U.S. Patent 5,055,851 to Sheffer taught that if three or more relationships
`have been established in a triangular space of three or more base stations (BSs) with a
`location database constructed having data related to possible mobile station (MS) locations,
`then arculation calculations may be performed, which use three distinct P or measurements to
`
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`determine an X, Y, two dimensional location, which can then be projected onto an area map.
`The triangulation calculation is based on the fact that the approximate distance of the mobile
`station (MS) from any base station (BS) cell can be calculated based on the received signal
`strength. Sheffer acknowledges that terrain variations affect accuracy, although as noted
`above, Sheffer's disclosure does not account for a sufficient number of variables, such as
`fixed and variable location shadow fading, which are typical in dense urban areas with moving
`traffic.
`
`Most field research before about 1988 has focused on characterizing (with the
`objective ofRF coverage prediction) the RF propagation channel (i.e., electromagnetic radio
`waves) using a single-ray model, although standard fit errors in regressions proved dismal
`(e.g., 40-80 dB). Later, multi-ray models were proposed, and much later, certain behaviors
`were studied with radio and digital channels. In 1981, Vogler proposed that radio waves at
`higher frequencies could be modeled using optics principles. In 1988 Walfisch and Bertoni
`applied optical methods to develop a two-ray model, which when compared to certain highly
`specific, controlled field data, provided extremely good regression fit standard errors of
`within 1.2 dB.
`In the Bertoni two ray model it was assumed that most cities would consist of a core
`of high-rise buildings surrounded by a much larger area having buildings of uniform height
`spread over regions comprising many square blocks, with street grids organizing buildings
`into rows that are nearly parallel. Rays penetrating buildings then emanating outside a
`building were neglected. Fig. BG-2 provides a basis for the variables.
`After a lengthy analysis it was concluded that path loss was a function of three
`factors: (1) the path loss between antennas in free space; (2) the reduction of rooftop wave
`fields due to settling; and (3) the effect of diffraction of the rooftop fields down to ground
`level. The last two factors were summarily termed Lex, given by:
`
`Lex = 57.1 + A+ log(/)+ R- ((18log(H )) - 18 log[1- ~](Equation 9)
`17H
`
`The influence of building geometry is contained in A:
`
`(Equation 1 0)
`
`However, a substantial difficulty with the two-ray model in practice is that it requires
`a substantial amount of data regarding building dimensions, geometries, street widths,
`antenna gain characteristics for every possible ray path, etc. Additionally, it requires an
`inordinate amount of computational resources and such a model is not easily updated or
`maintained.
`Unfortunately, in practice clutter geometries and building heights are random.
`Moreover, data of sufficient detail has been extremely difficult to acquire, and regression
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`standard fit errors are poor; i.e., in the general case, these errors were found to be 40-60 dB.
`Thus the two-ray model approach, although sometimes providing an improvement over single
`ray techniques, still did not predict RF signal characteristics in the general case to level of
`accuracy desired (<lOdB).
`Work by Greenstein has since developed from the perspective of measurement-based
`regression models, as opposed to the previous approach of predicting-first, then performing
`measurement comparisons. Apparently yielding to the fact that low-power, low antenna
`(e.g., 12-25 feet above ground) height PCS microcell coverage was insufficient in urban
`buildings, Greenstein, et al, authored "Performance Evaluations for Urban Line-of-sight
`Microcells Using a Multi-ray Propagation Model", in IEEE Globecom Proceedings, 12/91.
`This paper proposed the idea of formulating regressions based on field measurements using
`small PCS microcells in a lineal microcell geometry (i.e., geometries in which there is always
`a line-of-sight (LOS) path between a subscriber's mobile and its current microsite).
`Additionally, Greenstein studied the communication channels variable Bit-Error-Rate
`(BER) in a spatial domain, which was a departure from previous research that limited field
`measurements to the RF propagation channel signal strength alone. However, Greenstein
`based his finding on two suspicious assumptions: 1) he assumed that distance correlation
`estimates were identical for uplink and downlink transmission paths; and 2) modulation
`techniques would be transparent in terms of improved distance correlation conclusions.
`Although some data held very correlations, other data and environments produced poor
`results. Accordingly, his results appear unreliable for use in general location context.
`In 1993 Greenstein, et al, authored "A Measurement-Based Model for Predicting
`Coverage Areas of Urban Micro cells", in the IEEE Journal On Selected Areas in
`Communications, Vol. 11, No.7, 9/93. Greenstein reported a generic measurement-based
`model ofRF attenuation in terms of constant-value contours surrounding a given low-power,
`low antenna microcell environment in a dense, rectilinear neighborhood, such as New York
`City. However, these contours were for the cellular frequency band. In this case, LOS and
`non-LOS clutter were considered for a given microcell site. A result of this analysis was that
`RF propagation losses (or attenuations), when cell antenna heights were relatively low,
`provided attenuation contours resembling a spline plane curve depicted as an asteroid,
`aligned with major street grid patterns. Further, Greenstein found that convex diamond(cid:173)
`shaped RF propagation loss contours were a common occurrence in field measurements in a
`rectilinear urban area. The special plane curve asteroid is represented by the formula x2/3 +
`y2/3 = r2/3
`. However, these results alone have not been sufficiently robust and general to
`accurately locate an MS, due to the variable nature of urban clutter spatial arrangements ..
`At Telesis Technology in 1994 Howard Xia, et al, authored "Microcellular
`Propagation Characteristics for Personal Communications in Urban and Suburban
`Environments", in IEEE Transactions of Vehicular Technology, Vol. 43, No.3, 8/94, which
`performed measurements specifically in the PCS 1.8 to 1.9 GHz frequency band. Xia found
`corresponding but more variable outcome results in San Francisco, Oakland (urban) and the
`Sunset and Mission Districts (suburban).
`
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`2.2.4 Summary of Factors Affecting RF Propagation
`
`The physical radio propagation channel perturbs signal strength, frequency (causing
`rate changes, phase delay, signal to noise ratios (e.g., CII for the analog case, or Eb!No, RF
`energy per bit, over average noise density ratio for the digital case) and Doppler-shift. Signal
`strength is usually characterized by:
`·Free Space Path Loss (Lp)
`· Slow fading loss or margin (Lslow)
`·Fast fading loss or margin <Lrast)
`Loss due to slow fading includes shadowing due to clutter blockage (sometimes
`included in Lp ). Fast fading is composed of multi path reflections which cause: 1) delay
`spread; 2) random phase shift or Rayleigh fading; and 3) random frequency modulation due
`to different Doppler shifts on different paths.
`Summing the path loss and the two fading margin loss components from the above
`yields a total path loss of:
`Lrota! = Lp + Lslow + Lrast
`
`Referring to Fig. BG-3, the figure illustrates key components of a typical cellular and PCS
`power budget design process. The cell designer increases the transmitted power Prx by the
`shadow fading margin Lslow which is usually chosen to be within the 1-2 percentile of the
`slow fading probability density function (PDF) to minimize the probability of unsatisfactorily
`low received power level PRX at the receiver. The PRX level must have enough signal to noise
`energy level (e.g., 10 dB) to overcome the receiver's internal noise level (e.g., -118dBm in
`the case of cellular 0.9 GHz), for a minimum voice quality standard. Thus in the example PRX
`must never be below -108 dBm, in order to maintain the quality standard.
`Additionally the short term fast signal fading due to multipath propagation is taken
`into account by deploying fast fading margin Lrast, which is typically also chosen to be a few
`percentiles of the fast fading distribution. The 1 to 2 percentiles compliment other network
`blockage guidelines. For example the cell base station traffic loading capacity and network
`transport facilities are usually designed for a 1-2 percentile blockage factor as well.
`However, in the worst-case scenario both fading margins are simultaneously exceeded, thus
`causing a fading margin overload.
`In Roy, Steele's, text, Mobile Radio Communications, IEEE Press, 1992, estimates
`for a GSM system operating in the 1.8 GHz band with a transmitter antenna height of6.4m
`and an MS receiver antenna height of 2m, and assumptions regarding total path loss,
`transmitter power would be calculated as follows:
`
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`Property of Intellabs LLC: Confidential
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`Table 1: GSM Power Budget Example
`
`Parameter
`Lslow
`Lfast
`Llpath
`Min. RX pwr required
`
`dBm value Will r~uire
`14
`7
`110
`-104
`
`TXpwr 27
`dBm
`
`Steele's sample size in a specific urban London area of80,000 LOS measurements and data
`reduction found a slow fading variance of
`
`0" = 7d.B
`
`assuming lognormal slow fading PDF and allowing for a 1.4% slow fading margin overload,
`thus
`
`slow = 2a = 14dB
`The fast fading margin was determined to be:
`
`Lrast = 7dB
`
`In contrast, Xia's measurements in urban and suburban California at 1.8 GHz
`uncovered flat-land shadow fades on the order of25-30 dB when the mobile station (MS)