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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.

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Presentation on theme: "Introduction to RF Planning A good plan should address the following Issues : Provision of required Capacity. Optimum usage of available frequency spectrum."— Presentation transcript:

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2 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.

3 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

4 CELLULAR ENGINERING OBJECTIVES

5 COST JUSTIFICATION OF CELLULAR RNP

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9 DESIGN CONSTRAINTS

10 LICENSE CONDITIONS

11 MANUFACTURER SPECIFIC PARAMETERS

12 RADIO COMMUNICATION FUNDAMENTALS

13 QUALITY OF SERVICE SPECIFICATIONS

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15 DEFINITION OF COVERAGE QUALITY

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17 BLOCKING RATE ( Grade of Service, GOS )

18 CALL SUCCESS RATE

19 RADIO PLANNING METHODOLOGY

20 Introduction to RF Planning A simple Planning Process Description 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 Capacity Studies Plan verification Quality check Update documents Coverage &C/I study Search areas Implement Plan Monitor Network Optimize Network Customer Acquires sites Capacity Studies Coverage plan & Interference studies Frequency plans and interference Studies Antenna Systems BSS parameter planning Data base & documentation of approved sites Expansion Plans.

21 Introduction to RF Planning Data Acquisition OMC Statistics A Interface Drive Test Implemented Planning Data 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

22 Cell Planning Aspects

23 The Basic Cell Planning Process

24 Cell Planning Aspects

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26 A typical Power Budget RF Link BudgetULDL Transmitting EndMSBTS Tx Rf power output33 dBm43 dBm Body Loss-3 dB0 dB Combiner Loss0 dB0 Db Feeder Loss(@2 Db/100 M) 0 dB- 1.5 dB Connector loss0 dB- 2 Db Tx antenna gain0 dB17.5 dB EIRP30 dBm57 dBm

27 A typical Power Budget RF Link BudgetULDL Receiving EndMSBTS Rx sensitivity-107 dBm-102 dBm Rx antenna gain17.5 dBm0 dB Diversity gain3 Db0 dB Connector Loss- 2 dB0 dB Feeder loss- 1.5 dB0 dB Interference degradation margin 3 dB3 Db Body loss0 dB-3 dB Duplexer loss0 dB Rx Power-121 dBm-96 dBm Fade margin4 dB Reqd Isotropic Rx. Power-117 dBm-92 dBm Maximum Permis. Path los147 Db149 dB

28 Summary

29 Urban Propagation Environment

30 Propagation Environment Some Typical values for Building Attenuation Type of buildingAttenuation in dBs Farms, Wooden houses, Sport halls0-3 Small offices,Parking lots,Independent houses,Small apartment blocks 4-7 Row Houses, offices in containers, Offices, Apartment blocks 8-11 Offices with large areas12-15 Medium factories, workshops without roof tops windows 16-19 Halls of metal, without windows20-23 Shopping malls, ware houses, buildings with metals/glass 24-27

31 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

32 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 )

33 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.

34 Hata Urban Propagation Model The general path loss equation is given as :- Lp = Q1+Q2Log(f) – 13.82 Log(Hbts) - a(Hm)+{44.9-6.55 Log(Hbts)}Log(d)+Q0 Lp = L0 +10 r 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 = 3.2{log(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

35 Path Loss & Attenuation Slope The path loss equation can be rewritten as : Lp = L 0 + { 44.9 – 6.55 + 26.16 log (f) – 13.83 log (h BTS )-a(H m ) Where L 0 is = [69.55 + 26.16 log (f) – 13.82 log ( H BTS ) – A (H m ) Or more conveniently Lp = L 0 + 10 log(d) is the SLOPE and is = {44.9 – 6.55 log(h BTS )}/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.

36 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 h B BTS antenna height Hr height of roof h B difference between BTS height and roof top. Hm difference between mobile height and the roof top.

37 Non line of Sight Propagation The total path loss is given by: Lp = L FS +L RFT +L MDB L FS = Free space loss = 32.44+20 log(f) + 20 log(d) Where, L FS = Free space loss. L RFT = Rooptop diffraction loss. L MDB = Multiple diffraction due to surrounding buildings. L RFT = -16.9 – 10 log(w) +10log(f) +20log(^Hm)+L(0) Where hm=hr-hm L( ) = Losses due to elevation angle. L( ) = -10 + 0.357 ( -00) for 0< <35 2.5 +0.075 ( -35) for 35< <55 4.0 +0.114 ( -55) for 55< <90

38 Non line of Sight Propagation The losses due to multiple diffraction and scattering components due to building are given by : L MBD = k 0 + ka +kd.log(d) +kf.log(f) – 9.log(w) Where K0 = - 18 log (1+ h B ) Ka = 54 – 0.8 ( h B ) Kd = 18 – 15 ( h B /hr) Kf = - 4 +0.7 {f/925) – 1 } for suburban areas Kf = - 4 +1.5 {f/925) – 1 } for urban areas W= street width h B = h B –hr For simplified calculation we can assume ka = 54 and kd = 18

39 Choice of Propagation Model Environment Type Model Dense Urban Street Canyon propagationWalfish Ikegami,LOS Non LOS Conditions, Micro cellsCOST231 Macro cells,antenna above rooftop Okumara-Hata Urban Urban AreasWalch-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

40 Calculation of Mobile Sensitivity. The Noise level at the Receiver side as follows: N R = KTB Where, K is the Boltzmanns constant = 1.38x10 -20 (mW/Hz/ 0 Kelvin) T is the receiver noise temperature in 0 Kelvin B is the receiver bandwidth in Hz.

41 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 )

42 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.

43 Probability Density Function Plotting the PDF : A PLOT OF RSS FOR A NUMBER OF SAMPLES

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

45 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 :

46 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

47 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-RSS MEAN ) 2 +…..+(RSSN- RSS MEAN ) 2 /(N-1)}]

48 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=

49 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- ) < z < (m+ )] = 68.26 % ; this means we are 68.34 % confident. P[(m- ) < z < (m+ )] = 95.44 % ; this means we are 95.44 % confident P[(m- ) < z < (m+ )] = 99.72 % ; this means we are 99.72 % confident. This is basically the area under the Normal Curve.

50 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..

51 Calculation of Fade Margin To calculate the fade margin we need to know : Propagation constant( ) >From formulae for the Model chosen >Or from the drive test plots Area probability : >A design objective usually 90 % Standard Deviation( ) >Calculated from the drive test results using statistical formulae or >Assumed for different environments. To use Jakes curves and tables.

52 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

53 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).

54 In Building Coverage Recalculate Fade Margin. >Involves separate propagation tests in buildings. >Calculate and for the desired coverage ( say 75 % or 50% ) >Use Jakes Curves and tables to calculate Fade Margin. >Often adequate data is not available for calculating the fade margin accurately. >Instead use typical values. Typical values for building penetration loss : Area75 % coverage50 % coverage Central business area< 20 dB< 15 dB Residential area< 15 dB< 12 dB Industrial area< 12 dB< 10 dB In Car6 to 8 dB

55 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 :

56 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 = i 2 + i j + j 2 Where I & j are integers.

57 Cell Planning and C/I Issues R D

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61 Frequency Planning Aspects

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66 Antenna Considerations

67 Tackling Multipath Fading

68 Diversity Antenna Systems

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72 General Antenna Specifications

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74 RADIO PLANNING METHODOLOGY

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76 COVERAGE PLANNING STRATEGIES

77 RADIO PLANNING METHODOLOGY

78 METHODOLOGY EXPLAINED

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81 RF Planning Process

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86 RF Planning Surveys

87 RF Propagation Test Kits

88 RF Planning Tool

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91 Model Calibration

92 Link Budget and other Steps

93 Capacity Calculations

94 Fine Tune The Plan

95 Site Selection

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97 Extending Cell Range

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