Introduction to RF Planning

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Introduction to RF Planning

• A good plan should address the following Issues
• Provision of required Capacity.
• Optimum usage of available frequency spectrum.
• Minimum number of sites.
• Provision for easy and smooth expansion of the
  Network in future.
• Provision of adequate coverage.

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Introduction to RF Planning

• In general a planning process starts with the
  inputs from the customer. The customer inputs
  include customer requirements, business plans,
  system characteristics, and any other
  constraints.
• After the planned system is implemented, the
  assumptions made during the planning process need
  to be validated and corrected wherever necessary
  through an optimization process.
• We can summarize the whole planning process under
  the 4 broad headings
• Capacity planning
• Coverage planning
• Parameter planning
• Optimization

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CELLULAR ENGINERING OBJECTIVES
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COST JUSTIFICATION OF CELLULAR RNP
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COST JUSTIFICATION OF CELLULAR RNP
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COST JUSTIFICATION OF CELLULAR RNP
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COST JUSTIFICATION OF CELLULAR RNP
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DESIGN CONSTRAINTS
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LICENSE CONDITIONS
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MANUFACTURER SPECIFIC PARAMETERS
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RADIO COMMUNICATION FUNDAMENTALS
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QUALITY OF SERVICE SPECIFICATIONS
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QUALITY OF SERVICE SPECIFICATIONS
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DEFINITION OF COVERAGE QUALITY
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DEFINITION OF COVERAGE QUALITY
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BLOCKING RATE ( Grade of Service, GOS )
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CALL SUCCESS RATE
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RADIO PLANNING METHODOLOGY
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Introduction to RF Planning

• A simple Planning Process Description

Capacity Studies
Business plan. No of Subs. Traffic per Subs. Subs
distribution Grade of service. Available
spectrum. Frequency Reuse. Types of coverage RF
Parameters Field strength studies Available
sites Site survey
Plan verification Quality check Update documents
Implement Plan
Monitor Network
Coverage C/I study Search areas
Optimize Network
Capacity Studies Coverage plan Interference
studies Frequency plans and interference
Studies Antenna Systems BSS parameter
planning Data base documentation of approved
sites Expansion Plans.
Customer Acquires sites
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Introduction to RF Planning
Implemented Planning Data
Data Acquisition OMC Statistics
A Interface Drive Test
Data Evaluation
Implemented Recommendation
Recommendations Change frequency plan Change
antenna orientation/Down tilt Change BSS
Parameters Dimension BSS Equipment Add new cells
for coverage Interference reduction Blocking
reduction Augment E1 links from MSC to PSTN
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Cell Planning Aspects
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The Basic Cell Planning Process
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Cell Planning Aspects
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Cell Planning Aspects
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A typical Power Budget
RF Link Budget UL DL
Transmitting End MS BTS

Tx Rf power output 33 dBm 43 dBm
Body Loss -3 dB 0 dB
Combiner Loss 0 dB 0 Db
Feeder Loss(_at_2 Db/100 M) 0 dB - 1.5 dB
Connector loss 0 dB - 2 Db
Tx antenna gain 0 dB 17.5 dB
EIRP 30 dBm 57 dBm
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A typical Power Budget
RF Link Budget UL DL
Receiving End MS BTS
Rx sensitivity -107 dBm -102 dBm
Rx antenna gain 17.5 dBm 0 dB
Diversity gain 3 Db 0 dB
Connector Loss - 2 dB 0 dB
Feeder loss - 1.5 dB 0 dB
Interference degradation margin 3 dB 3 Db
Body loss 0 dB -3 dB
Duplexer loss 0 dB 0 dB
Rx Power -121 dBm -96 dBm
Fade margin 4 dB 4 dB
Reqd Isotropic Rx. Power -117 dBm -92 dBm
Maximum Permis. Path los 147 Db 149 dB
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Summary
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Urban Propagation Environment
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Propagation Environment

• Some Typical values for Building Attenuation

Type of building Attenuation in dBs
Farms, Wooden houses, Sport halls 0-3
Small offices,Parking lots,Independent houses,Small apartment blocks 4-7
Row Houses, offices in containers, Offices, Apartment blocks 8-11
Offices with large areas 12-15
Medium factories, workshops without roof tops windows 16-19
Halls of metal, without windows 20-23
Shopping malls, ware houses, buildings with metals/glass 24-27
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Propagation Models

• Classical Propagation models -
• Log Distance propagation model
• Longley Rice Model (Irregular terrain model )
• Okumara
• Hata
• Cost 231 Hata (Similar to Hata, for 1500-2000
  MHz band
• Walfisch Ikegami Cost 231
• Walfisch-Xia JTC
• XLOS (Motorola proprietary Model )
• Bullington
• Du path Loss Model
• Diffracting screens model

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Propagation Models

• Important Propagation models -
• Okumara Hata model (urban / suburban areas )( GSM
  900 band )
• Cost 231 Hata model (GSM 1800 band )
• Walfisch Ikegami Model (Dense Urban / Microcell
  areas )
• XLOS (Motorola proprietary Model )

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Okumara Hata Models

• In the early 1960 , a Japanese scientist by name
  Okumara conducted extensive propagation tests for
  mobile systems at different frequencies. The test
  were conducted at 200, 453, 922, 1310, 1430 and
  1920 Mhz.
• The test were also conducted for different BTS
  and mobile antenna heights, at each frequency,
  over varying distances between the BTS and the
  mobile.
• The Okumara tests were valid for
• 150-2000 Mhz.
• 1-100 Kms.
• BTS heights of 30-200 m.
• MS antenna height, typically 1.5 m. (1-10 m.)
• The results of Okumara tests were graphically
  represented and were not easy for computer based
  analysis.
• Hata took Okumaras data and derived a set of
  empirical equations to calculate the path loss in
  various environments. He also suggested
  correction factors to be used in Quasi open and
  suburban areas.

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Hata Urban Propagation Model

• The general path loss equation is given as -
• Lp Q1Q2Log(f) 13.82 Log(Hbts) -
  a(Hm)44.9-6.55 Log(Hbts)Log(d)Q0
• Lp L0 10r Log (d) path loss in dB
• F frequency in Mhz.
• D distance between BTS and the mobile (1-20
  Kms.)
• Hbts Base station height in metres ( 30 to 100
  m )
• A(hm) 1.1log(f) - 0.7 hm - 1.56log(f) - 0.8
  for Urban areas and
• 3.2log(11.75 hm)2 - 4.97 for dense urban
  areas.
• Hm mobile antenna height (1-10 m)
• Q1 69.55 for frequencies from 150 to 1000 MHz.
• 46.3 for frequencies from 1500 to 2000
  MHz.
• Q2 26.16 for frequencies from 150 to 1000 MHz.
• 33.9 for frequencies from 1500 to 2000
  MHz.
• Q0 0 dB for Urban
• 3 dB for Dense Urban

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Path Loss Attenuation Slope

• The path loss equation can be rewritten as
• Lp L0 44.9 6.55 26.16 log (f) 13.83
  log (hBTS)-a(Hm)
• Where L0 is 69.55 26.16 log (f) 13.82 log
  ( HBTS ) A (Hm)
• Or more conveniently
• Lp L0 10 log(d)
• is the SLOPE and is 44.9 6.55
  log(hBTS)/10
• Variation of base station height can be plotted
  as shown in the diagram.
• We can say that Lp 10 log(d)
• typically varies from 3.5 to 4 for urban
  environment.
• When the environment is different, then we have
  to choose models fitting the environment and
  calculate the path loss slope. This will be
  discussed subsequently.

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Non line of Sight Propagation

• Here we assume that the BTS antenna is above roof
  level for any building within the cell and that
  there is no line of sight between the BTS and the
  mobile
• We define the following parameters with reference
  to the diagram shown in the next slide
• W the distance between street mobile and
  building
• Hm mobile antenna height
• hB BTS antenna height
• Hr height of roof
• hB difference between BTS height and roof
  top.
• Hm difference between mobile height and the
  roof top.

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Non line of Sight Propagation

• The total path loss is given by
• Lp LFSLRFTLMDB
• LFS Free space loss 32.4420 log(f) 20
  log(d)
• Where,
• LFS Free space loss.
• LRFT Rooptop diffraction loss.
• LMDB Multiple diffraction due to surrounding
  buildings.
• LRFT -16.9 10 log(w) 10log(f)
  20log(Hm)L(0)
• Where
• hmhr-hm
• L( ) Losses due to elevation angle.
• L( ) -10 0.357 ( -00) for 0lt lt35
• 2.5 0.075 ( -35) for 35lt lt55
• 4.0 0.114 ( -55) for 55lt lt90

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Non line of Sight Propagation

• The losses due to multiple diffraction and
  scattering components due to building are given
  by
• LMBD k0 ka kd.log(d) kf.log(f) 9.log(w)
• Where
• K0 - 18 log (1 hB)
• Ka 54 0.8 ( hB)
• Kd 18 15 ( hB/hr)
• Kf - 4 0.7 f/925) 1 for suburban areas
• Kf - 4 1.5 f/925) 1 for urban areas
• W street width
• hB hB hr
• For simplified calculation we can assume ka 54
  and kd 18

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Choice of Propagation Model
Environment Type Model
Dense Urban
Street Canyon propagation Walfish Ikegami,LOS
Non LOS Conditions, Micro cells COST231
Macro cells,antenna above rooftop Okumara-Hata
Urban
Urban Areas Walch-ikegami
Mix of Buildings of varying heights, vegetation, and open areas. Okumara-Hata
Sub urban
Business and residential,open areas. Okumara Hata
Rural
Large open areas,fields,difficult terrain with obstacles. Okumara-Hata
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Calculation of Mobile Sensitivity.

• The Noise level at the Receiver side as follows
• NR KTB
• Where,
• K is the Boltzmanns constant 1.38x10-20
  (mW/Hz/0Kelvin)
• T is the receiver noise temperature in 0Kelvin
• B is the receiver bandwidth in Hz.

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Signal Variations

• Fade margin becomes necessary to account for the
  unpredictable changes in RF signal levels at the
  receiver. The mobile receive signal contains 2
  components
• A fast fading signal (short term fading )
• A slow fading signal (long term fading )

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Probability Density Function

• The study of radio signals involve actual
  measurement of signal levels at various points
  and applying statistical methods to the available
  data.
• A typical multipath signal is obtained by
  plotting the RSS for a number of samples.
• We divide the vertical scale in to 1 dB bin and
  count number of samples is plotted against RF
  level . This is how the probability density
  function for the receive signal is obtained.
• However, instead of such elaborate plotting we
  can use a statistical expression for the PDF of
  the RF signal given by
• P(y) 1/2 e - ( - y m )2 / 2 ( )2
• Where y is the random variable (the measured RSS
  in this case ), m is the mean value of the
  samples considered and y is the STANDARD
  DEVIATION of the measured signal with reference
  to the mean .
• The PDF obtained from the above is called a
  NORMAL curve or a Gaussian Distribution. It is
  always symmetrical with reference to the mean
  level.

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Probability Density Function

• Plotting the PDF

A PLOT OF RSS FOR A NUMBER OF SAMPLES
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Probability Density Function

• Plotting the PDF

P(x) ni/N Ni number of RSS within 1 dB bin
for a given level.
NORMAL DISTRIBUTION
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Probability Density Function

• A PDF of random variable is given by
• P(y) ½ e - (y-m)2 / 2( )2
• Where, y is the variable, m is the mean value and
  is the Standard Deviation of the variable
  with reference to its mean value.
• The normal distribution (also called the Gaussian
  Distribution ) is symmetrical about the mean
  value.
• A typical Gaussian PDF

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Probability Density Function

• The normal Distribution depends on the value of
  Standard Deviation
• We get a different curve for each value of
• The total area under the curve is UNITY

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Calculation of Standard Deviation

• If the mean of n samples is m, then the
  standard deviation is given by
• Square root of (x1-m)2 ..( xn-m)2
  /(n-1)
• Where n is the number of samples and m is the
  mean.
• For our application we can re write the above
  equation as
• Square root of RSS1-RSSMEAN)2..(RSSN-RSS
  MEAN)2/(N-1)

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Confidence Intervals

• The normal of the Gaussian distribution helps us
  to estimate the accuracy with which we can say
  that a measured value of the random variable
  would be within certain specified limits.
• The total area under the Normal curve is treated
  as unity. Then for any value of the measured
  value of the variable, its probability can be
  expressed as a percentage.
• In general, if m is mean value of the random
  variable within normal distribution and is the
  Standard Deviation, then,
• The probability of occurrence of the sample
  within m and any value of x of the variable is
  given by
• P
• By setting (x-m)/ z, we get,
• P

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Confidence Intervals

• The value of P is known as the Probability
  integral or the ERROR FUNCTION
• The limits (m n )are called the confidence
  intervals.
• From the formula given above, the probability
• P(m- ) lt z lt (m ) 68.26 this means we
  are 68.34 confident.
• P(m- ) lt z lt (m ) 95.44 this means
  we are 95.44 confident
• P(m- ) lt z lt (m ) 99.72 this means
  we are 99.72 confident.
• This is basically the area under the Normal Curve.

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The Concept of Normalized Standard Deviation

• The probability that a particular sample lies
  within specified limits is given by the equation
  
• P
• We define z (x-m)/ as the Normalized Standard
  Deviation.
• The probability P could be obtained from Standard
  Tables (available in standard books on statistics
  ).
• A sample portion of the statistical table is
  presented in the next slide..

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Calculation of Fade Margin

• To calculate the fade margin we need to know
• Propagation constant(?)
• gtFrom formulae for the Model chosen
• gtOr from the drive test plots
• Area probability
• gtA design objective usually 90
• Standard Deviation(?)
• gtCalculated from the drive test results using
  statistical formulae or
• gtAssumed for different environments.
• To use Jakes curves and tables.

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Calculation of Edge Probability and Fade Margin

• From the values of ? and ? we calculate
• ? ? / ?
• Find edge probability from Jakes curves for a
  desired coverage probability, against the value
  of on the x axis.
• Use Jakes table to find out the correlation
  factor required
• Look for the column that has value closest to the
  edge probability and read the correlation factor
  across the corresponding row.
• Multiply ? by the correction factor to get the
  Fade Margin.
• Add Fade Margin to the RSS calculated from the
  power budget

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Significance Of Area and Edge Probabilities

• Required RSS is 85 dBm.
• Suppose the desired coverage probability is 90
  and the edge probability from the Jakes curves is
  0,75
• This means that the mobile would receive a signal
  that is better than 85 dBm in 90 of the area
  of the cell
• At the edges of the cell, 75 of the calls made
  would have this minimum signal strength (RSS).

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In Building Coverage

• Recalculate Fade Margin.
• gtInvolves separate propagation tests in
  buildings.
• gtCalculate and for the desired coverage (
  say 75 or 50 )
• gtUse Jakes Curves and tables to calculate Fade
  Margin.
• gtOften adequate data is not available for
  calculating the fade margin accurately.
• gtInstead use typical values.
• Typical values for building penetration loss

Area 75 coverage 50 coverage
Central business area lt 20 dB lt 15 dB
Residential area lt 15 dB lt 12 dB
Industrial area lt 12 dB lt 10 dB
In Car 6 to 8 dB 6 to 8 dB
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Fuzzy Maths and Fuzzy Logic

• The models that we studied so far are purely
  empirical.
• The formulas we used do not all take care of all
  the possible environments.
• Fuzzy logic could be useful for experienced
  planners in making right guesses.
• We divide the environment into 5 categories viz.,
  Free space, Rural, Suburban, urban, and dense
  urban.
• We divide assign specific attenuation constant
  values to each categories , say
• Fuzzy logic helps us to guess the right value for
  , the attenuation constant for an environment
  which is neither rural nor suburban nor urban but
  a mixture, with a strong resemblance to one of
  the major categories.
• The following simple rules can be used
• Mixture of Free space and Rural
• Mixture of Rural and Suburban
• Mixture of Suburban and Urban
• Mixture of Urban and Dense urban

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Cell Planning and C/I Issues

• The 2 major sources of interference are
• Co Channel Interference.
• Adjacent Channel Interference.
• The levels of these Interference are dependent on
  
• The cell radius
• The distance cells (D)
• The minimum reuse distance (D) is given by
• D ( 3N )½ R
• Where N Reuse pattern
• i2 i j j2
• Where I j are integers.

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Cell Planning and C/I Issues
R
D
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Cell Planning and C/I Issues
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Cell Planning and C/I Issues
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Cell Planning and C/I Issues
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Frequency Planning Aspects
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Frequency Planning Aspects
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Frequency Planning Aspects
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Frequency Planning Aspects
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Frequency Planning Aspects
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Antenna Considerations
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Tackling Multipath Fading
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Diversity Antenna Systems
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Diversity Antenna Systems
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Diversity Antenna Systems
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Diversity Antenna Systems
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General Antenna Specifications
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General Antenna Specifications
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RADIO PLANNING METHODOLOGY
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RADIO PLANNING METHODOLOGY
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COVERAGE PLANNING STRATEGIES
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RADIO PLANNING METHODOLOGY
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METHODOLOGY EXPLAINED
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METHODOLOGY EXPLAINED
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METHODOLOGY EXPLAINED
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RF Planning Process
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RF Planning Process
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RF Planning Process
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RF Planning Process
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RF Planning Process
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RF Planning Surveys
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RF Propagation Test Kits
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RF Planning Tool
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RF Planning Tool
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RF Planning Tool
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Model Calibration
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Link Budget and other Steps
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Capacity Calculations
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Fine Tune The Plan
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Site Selection
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Site Selection
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Extending Cell Range
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Extending Cell Range
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Extending Cell Range
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Extending Cell Range