RFGuidelineModelCalibration

RF Guideline Propagation Model Tuning

RF Guideline Propagation Model Tuning In ASSET

Author: Doc-ID: Date:

H.H. Rhrig Lucent Technologies Proprietary RFET-QA-REP-00-010-V01.00 12 September 00 Use Pursuant to Company Instructions

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1.1 1 of 53


Document History Version 1.0 Date 06/01 Author(s) Hans-Hubert Rhrig Change Description First draft.



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CONTENTS1 2 3 INTRODUCTION ................................ ................................ ................................ ............................. 4 WHY TUNING A PROPAGATION MODEL? ................................ ................................ ................... 5 IN COMMON USE PROPAGATION MODELS ................................ ................................ ................ 6 3.1 OKUMURA-HATA-MODEL................................ ................................ ................................ .............. 6 3.2 COST231-HATA MODEL ................................ ................................ ................................ ............. 7 3.3 RACE-1043 CLUTTER MODEL ................................ ................................ ................................ ..... 8 3.4 EXTRA DETERMINISTIC METHODS ................................ ................................ ................................ . 9 3.4.1 Common in use Knife-edge Diffraction Methods ................................ ................................ . 9 3.4.2 Effective Antenna Height Calculation................................ ................................ ................ 11 4 INDICATORS OF PREDICTION M ODEL PERFORMANCE ................................ .......................... 12 4.1 BASIC STATISTICS ................................ ................................ ................................ ..................... 12 4.2 PREDICTION ERROR STATISTICS OF AIRCOM INTERNATIONAL ASSET ................................ .............. 13 4.2.1 Displaying Prediction Error in the 2D-View ................................ ................................ ...... 13 4.2.2 Displaying Received Level/Prediction Error vs. Log(d)................................ ...................... 14 4.2.3 Asset Analyse Text File ................................ ................................ ................................ ... 15 4.3 PREDICTION ERROR STATISTICS OF MEAANALYSE................................ ................................ ........ 16 4.3.1 MeaAnalyse output file _Summary.txt ................................ ................................ ......... 16 4.3.2 MeaAnalyse feature StandardDeviationVsMeanError ................................ ....................... 18 4.3.3 MeaAnalyse feature(s) ...versus Distance ................................ ................................ ........ 19 5 INPUT DATA ................................ ................................ ................................ ................................ . 20 5.1 MAP DATA................................ ................................ ................................ ................................ 20 5.1.1 Paper Maps ................................ ................................ ................................ ..................... 20 5.1.2 Topographical Database ................................ ................................ ................................ .. 21 5.2 CW SURVEY DATA ................................ ................................ ................................ .................... 22 5.3 START PARAMETER VALUES OF PROPAGATION MODEL ................................ ................................ . 23 5.3.1 Aircom ASSET Standard Macrocell Model ................................ ................................ ....... 23 5.3.2 Classification of Hata Adjustment Coefficients to ASSET k-parameter.............................. 24 5.3.3 Enhancement of the ASSET Standard Macrocell Model................................ ................. 25 Clutter Category................................ ................................ ................................ ............................. 27 6 WHICH COEFFICIENTS ARE TUNABLE? ................................ ................................ ................... 28 6.1 ADJUSTMENT COEFFICIENTS OF HATA-MODELS ................................ ................................ ............ 28 6.1.1 Intercept C1 and Frequency Coefficient C2 ................................ ................................ ....... 29 6.1.2 Base Station Heights Adjustment Coefficients C3 ................................ ............................. 30 6.1.3 Path Loss Slope................................ ................................ ................................ ............... 31 6.1.4 Mobile Antenna Height Correction ................................ ................................ .................... 36 6.1.5 Clutter Adjustment L C ................................ ................................ ................................ ....... 37 6.1.6 Diffraction Loss LD and Adjustment Coefficient C6 ................................ ............................ 38 7 THE CALIBRATION PROCESS ................................ ................................ ................................ .... 39 7.1 SORT CW MEASUREMENT DATA ................................ ................................ ................................ 40 7.2 F IRST CW MEASUREMENT ANALYSIS ................................ ................................ .......................... 40 7.3 F IND BEST SUITED EFFECTIVE ANTENNA HEIGHT CALCULATION METHOD ................................ ........ 40 7.4 T UNE BASE STATION HEIGHT ADJUSTMENT COEFFICIENTS ................................ .............................. 41 7.4.1 Tune base station height adjustment coefficient k5 ................................ ........................... 41 7.4.2 Tune base station height with distance adjustment coefficient k6 ................................ ...... 42 7.5 T UNE INTERCEPT AND SLOPE COEFFICIENT ................................ ................................ .................. 42 7.5.1 Intercept ................................ ................................ ................................ .......................... 43 7.5.2 Slope ................................ ................................ ................................ ............................... 44 7.5.3 Near/Far Intercept and Slope Coefficients ................................ ................................ ........ 44Author: Doc-ID: Date: H.H. Rhrig Lucent Technologies Proprietary RFET-QA-REP-00-010-V01.00 12 September 00 Use Pursuant to Company Instructions Revision: Page: 1.1 2 of 53


7.6 7.7 7.8 7.9 8

T UNE CLUTTER OFFSETS................................ ................................ ................................ ........... 45 F IND BEST SUITED KNIFE-EDGE DIFFRACTION METHOD ................................ ................................ ... 46 T UNE THE DIFFRACTION ADJUSTMENT COEFFICIENT................................ ................................ ....... 46 REANALYZE, F INE TUNING ................................ ................................ ................................ .......... 47

HOW TO USE MEAANALYSE ................................ ................................ ................................ ...... 47 8.1 8.2 8.3 8.4 GET THE BIN INFORMATION ................................ ................................ ................................ ........ 47 GET SPREAD SHEETS WITH MEAANALYSE ................................ ................................ ................... 50 CREATE CHARTS BY EXCEL ................................ ................................ ................................ ........ 50 CREATE CHART STANDARD DEVIATION VS. MEAN ERROR IN EXCEL ................................ ................ 52

9

MATH BASICS ................................ ................................ ................................ .............................. 55 9.1 9.2 STATISTIC BASICS ................................ ................................ ................................ ..................... 55 LOGARITHMIC BASICS ................................ ................................ ................................ ................ 56



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1

Introduction

To implement a mobile radio system, wave propagation models are necessary to determine propagation characteristics for any arbitrary installation. Predictions are required for a proper coverage planning, for interference analysis as well as for cell calculations, which are the basis for the RF network design and optimization purposes. However, the radio propagation channel is a very critical component for mobile radio communications systems. The field strength level, at a given point, not only depends on its distance from the transmitter, the frequency of transmission and the antenna heights but also on the long-term and short-term interferences caused by reflections of the natural environment (terrain configuration, vegetation) and the man-made environment. This influences the wave propagation in different ways. Well-known empirical path loss prediction models like the model of Okumura -Hata or the COST231Hata model estimates the median signal strength in a small area and do not consider the path specific propagation effects by detailed analytical expressions. The Hata models (or other empirical methods) only use simple empirical expressions extracted from curves get from the analysis of measurement data. This has the advantage of implicitly taking all path specific propagation effects of the environment (known or unknown) into account mentioned above. However, each region or country and in the end each city has the own specific character of topography, vegetation and man-made structure have an effect on the wave propagation. Therefore, empirical models must always be subjected to stringent validation by testing it on measurement data sets collected at locations and conditions (as well as at transmission frequencies) which are in many cases other than used to produce the model in the first place. The overall objective of the tuning process is to adapt the propagation model to the local environments characterized by CW measurement data, in conjunction with the specific classification of the actual terrain database. But a tuned propagation model is only good as the input data used to calibrate it. Consequently, the results of the tuning process depends on quality and quantity of the CW measurements, on the quality of used terrain database as well as on the ability of the RF Planning tool to support the user with suitable applications to the CW measurement analysis process. Furthermore, the person who will carry out the tuning process should have knowledge about the basic mathematics and the basic wave propagation mechanism in different mediums as well as knows the common in use propagation models, effective antenna height calculation as well as knife-edge diffraction methods. The purpose of this paper is not to describe the perfect way of tuning empirical propagation models, because there is no single correct way or ideal method. This paper tries to give recommendations and methods useful for the tuning process with help of the Aircom Asset CW Measurement Analyse Tool.



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2

Why tuning a propagation model?

The overall object of tuning a propagation model is to adapt the path loss prediction model to the local environments and the specific classification of the actual terrain database to improve the coverage estimation (path loss prediction). Because: y Each region or country has the own specific character of vegetation and man -made structure that influence the wave propagation on different ways. The Hata models based on the Okumura technique adopts curves for urban areas based on the type and density of buildings in Tokyo and it may not be transferable to cities in Europe or North America. Indeed, experience with CW measurements in the USA (e.g. South Carolina, Indianapolis and Boston) have shown that the typical US urban environment lies is similar to Okumuras definition of suburban. Empirical path loss prediction models like the COST231-Hata model (see next chapter) are restricted to flat terrain. In case of wavy (hilly) terrain or topographical obstacles like mountains (obstruct the line of sight between BS and MS) the Hata model has to combine by extra deterministic methods have to use like knife-edge and/or effective antenna height calculation to consider the influence of topography. Usually empirical models are restricted to ranges of frequencies, antenna heights and distances. If the parameter of the planned base stations are outside these limitations, then the empirical model have to extent by analyzing CW measurements. High-resolution terrain databases (e.g. pixel size is from 20 meter up to 30 meter) are created by satellite images (typical 10 meter resolution). However, the clutter database is the result of a person, who interprets groups or cluster of gray-pattern in the image and assign the marked area to the most likely suitable clutter category. Furthermore, a satellite image provides geo information about the local density and extent of buildings, but it cannot give information about the local building heights that also impact on wave propagation. Consequently, the path loss prediction has to adapt to the topographical database by the help of CW measurements. Some RF planning tools support extra clutter attributes like clutter heights and separation. Using these features can improve the accuracy of the coverage prediction. It is recommended to validate the specified clutter information by CW measurements.

y

y

y

y

Note:

Keep in mind, that the fitted propagation model is only applicable to the local terrain database that was used for the model tuning.



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3

In Common use Propagation Models

This chapter describes well-known propagation models used for the coverage analysis in Macrocell environments like the model of Okumura-Hata, the COST231-Hata model and the RACE model. The propagation models mentioned below estimate the path loss by empirical information. The empirical information based on the analysis of RF propagation measurements. Empirical models calculate the median signal for each pixel and cannot determine the local mean signal that result from local effects of various multi-path phenomena. The local mean signal levels have to distribute around the pixel median with a log -normal probability distribution.

3.1

Okumura-Hata-Model

The Okumura-Hata prediction model is based on empirical information obtained from measurements in Japan (Okumura 1965). From the results of these measurements propagation curves in the frequency ranges from 200 [MHz] up to 2[GHz] depending from the distance (1- 100 [km]) to the transmitter have been extracted. Curves are given for effective base station antenna heights in the range 30 [m] 1000 [m] and for a mobile station antenna height of 1.5 [m]. The Hata formula (1980) is a mathematical fit for the Okumura graphical measurement results. Four parameters are used for estimation of the propagation loss by Hata's well known model: frequency f, distance d, base station antenna height hBS and the height of the mobile antenna hMS. In Hatas model, which is based on Okamuras various correction functions the basic transmission loss, Lb, in urban areas is: h BS f ! 69.55 26.16 lg 13.82 lg [m] [MHz] [dB ] Lb Where: f hBS hMS d a(hMS) The model is restricted to: Frequency f : Base station antenna height hBS: Receiver antenna height hMS: Distance d from the site : 150 - 1500 [MHz] 30 - 200 [m] 1 - 10 [m] 1 - 20 [km] frequency in [MHz] base station antenna height in [m] mobile antenna height in [m] distance between base station and mobile station in [km] mobile antenna height correction in [dB] h 44.9 6.55 lg BS [m] lg d a h [km] MS

(3.1.1)

The model of Okumura-Hata is restricted to quasi-smooth terrain where the average height of terrain does not change more than 20 [m] and the actual elevations of the path profile undulate in a range of no more than 10 m due to the average height. Furthermore, the model of Okumura-Hata is limited to large and small macro-cells, i. e. base station antenna heights above rooftop levels adjacent to the base station.Author: Doc-ID: Date: H.H. Rhrig Lucent Technologies Proprietary RFET-QA-REP-00-010-V01.00 12 September 00 Use Pursuant to Company Instructions Revision: Page: 1.1 6 of 53


The Okumura-Hata formula is quite good in urban and suburban areas. However, in rural areas over irregular terrain there is a tendency to be too optimistic.

Additional the model of Okumura-Hata considers the effects due to land usage in the vicinity of the MS by empirical corrections (recommended by ETSI). The clutter correction for suburban area is defined by: L Suburban [dB ] d ! Lb 2 lg f 28[MHz] 2 5.4 (3.1.2)

The clutter correction for rural (quasi-open) area is defined by:

[dB]

b

The clutter correction for rural (open) area is defined by:

[d ]

As L0 in equation (3.1.1) only applicable for a mobile antenna height hMS=1.5 [m]. For other values of hMS the term a(hMS) is a correction of the path loss L0. The corrections to the mobile antenna height correction depends on the frequency range and the land usage in the vicinity of the mobile station. If the mobile station be in urban environment the adjustment to the mobile antenna height is defined by:

In su ur an or rural environment the loss correction to the mobile antenna height is defined by: ah MS Suburban ,Rural [dB ]

3.2

COST231-Hata Model

The COST-231 group has extended Hatas model to the frequency band from 1500 [MHz] up to 2000 [MHz] by analyzing Okumuras propagation curves in the upper frequency band. The repeated analysis of the measured propagation curves of Okumura within this fr quency range e resulted in a change of the term, which depends on the frequency. Additionally a new correction factor was introduced that increases the propagation path loss for metropolitan centers. This combination is called "COST231-Hata-Model". The basic path loss (Lb) in urban areas is:Author: Doc-ID: Date: H.H. Rhrig Lucent Technologies Proprietary RFET-QA-REP-00-010-V01.00 12 September 00 Use Pursuant to Company Instructions Revision: Page: 1.1 7 of 53

a hMS Ur an [dB ]

3.2 l

h 11.75 MS 4.97 [m]

1.1 l

hMS f [MHz] 0.7 [m ] 1.56 l

L

ural(open )

f d ! Lb 4.78 lg MHz 18.33 lg MHz 40.94

2

2

f [MHz] 0.8

Rural(quasi - open)

d

f 4.78 lg MHz

2

18.33 lg

f 35.94 z

(3.1.3)

(3.1.4)

(3.1.5)

(3.1.6)


Lb h BS f ! 46.30 33.90 lg 13.82 lg [m] [dB ] [MHz]

h 44.9 6.55 lg BS [m]

lg d a h [km] MS Cm

(3.2.1)

Where: f hBS hMS d Cm frequency in [MHz] base station antenna height in [m] mobile antenna height in [m] distance between base station and mobile station in [km] 0 [dB] for medium sized city and suburban centers with medium tree density or 3 [dB] for metropolitan centers

The mobile antenna height correction a(hMS) is defined by: hMS a hMS f f ! 1.1 lg [MHz] 0.7 [m] 1.56 lg [MHz] 0.8 [dB ] The COST231-Hata-Model is restricted to the following range of parameters: Frequency f : Base station antenna height hBS: Receiver antenna height hMS: Distance d from the site : 1500 - 2000 [MHz] 30 - 200 [m] 1 - 10 [m] 1 - 20 [km] (3.2.2)

The application of the COST231-Hata-Model is restricted to large and small macro-cells, i. e. base station antenna heights above rooftop levels adjacent to the base station. The clutter corrections (equations 3.1.2, 3.1.3 and 3.1.4) mentioned in the chapter before applicable to the COST231-Hata model as well.

3.3

RACE-1043 Clutter Model

The characterization (in view of different densities and/or heights of buildings and vegetation) of the environment (clutter category) in the vicinity of the mobile station is very important for the path loss estimation. Because, the median signal and the local mean signal distribution, at a given point, depends on the land usage like vegetation and/or man-made structure (buildings). The Okumura-Hata and COST231-Hata formulas treat only three different types of land usage (urban, suburban and rural). However, three land usage categories are not sufficient to characterize the effects due to land usage in the vicinity of the MS. Within the scope of the RACE-1043 working group, the three main clutter classes (urban, suburban and open field) have been subdivided into several clutter categories to distinguish between different densities and heights of vegetations and buildings. Table 1 shows the different clutter categories and the loss correction recommended by RACE-1043.Clutter type W O1 O2 F1 F2Author: Doc-ID: Date:

Description Water Open field, no obstructions Open field, few obstructions Forest, low density with small trees or bushes Forest, mostly higher and more densely packed treesRevision: Page:

Cm [dB] -29 -24 -19 -19 -91.1 8 of 53



S1 S2 S3 U1 U2

Suburban, low density Suburban, leafy buildings Suburban, density buildings Urban, low density, 2 - 5 floors Urban, high density, more than 5 floors

-11 -8 -5 -3 0

Table 1: Clutter correction factors recommended by RACE-1043

3.4

Extra Deterministic Methods

The model of Okumura-Hata and the COST231-Hata model restricted to flat terrain. Consequently, in case of wavy (hilly) terrain or topographical obstacles like mountains (obstruct the line of sight between BS and MS) the accuracy is generally decreased. Incorporating terrain information can make a substantial difference to the prediction. Therefore, extra deterministic methods have to use like knife-edge and/or effective antenna height calculation to consider the influence of topography. Empirical models combined with deterministic methods are semi-empirical propagation models. 3.4.1 Common in use Knife -edge Diffraction Methods

When topographical obstacles (hills, mountains) obstruct the line of sight between the base station antenna and mobile station additional diffraction losses occurs which depend on the height and the location of obstacles (see Figure 1).

Figure 1: None line of sight (NLOS) condition

3.4.1.1

Bullington Method

The Bullington method calculates the diffraction loss over multiple obstructions by considering a single equivalent knife-edge positioned at the point of intersection of the transmitter and receiver horizon paths. The total diffraction loss is taken as that over the equivalent knife-edge obstruction. This method has the advantage of being simple, but often significant obstacles can be ignored leading to an optimistic estimate of field strength. However, this knife-edge diffraction method achieve the lowest standard deviation in many cases. 3.4.1.2 Epstein-Peterson Method

The Epstein-Peterson technique is based on the assumption that the total loss can be evaluated as the sum of attenuation due to each respective significant obstruction.Author: Doc-ID: Date: H.H. Rhrig Lucent Technologies Proprietary RFET-QA-REP-00-010-V01.00 12 September 00 Use Pursuant to Company Instructions Revision: Page: 1.1 9 of 53


The diffraction loss from the obstacle is calculated by assuming that the receiver is at the second obstruction. The loss from the second obstacle is then calculated assuming the transmitter is at the first obstruction and the receiver at the third. Furthermore, the loss from a transmitter at the next obstacle to the receiver is calculated. The total Epstein-Peterson diffraction loss is given by the sum of all the losses calculated.

The Epstein-Peterson technique overcomes one of the problems of the Bullington metho namely that d, important obstacles can be ignored. However, it has been demonstrated that this method has limitations when the obstructions are closely spaced.

3.4.1.3

Japanese Atlas Method

The Japanese Atlas technique is similar to the Epstein-Peterson method and was proposed by the Japanese postal service. Again it is based on the assumption that the total loss can be evaluated as the sum of attenuation due to each obstruction. However, in contrast to the Epstein-Peterson method the effective source is not the top of the preceding obstruction but the projection of the horizon ray for the obstruction to a point on the vertical plane through one of the terminals. This method gives improved results when the obstructions are closely spaced. 3.4.1.4 Deygout Method

The Deygout technique calculates a v-parameter for each edge, the one with the largest is termed the main edge and its loss calculated in the standard way. Additional losses for other obstructions are calculated between the main edge and the obstructed terminal. The total Deygout loss is given by the sum of all losses calculated. In order to extend the technique to many obstructions it is necessary to employ sub-main edges. These are the next most significant edge(s) at either side of the main edge. The loss form the sub-main-edge is calculated assuming a hypothetical terminal located at the main -edge (ignoring any less significant edges). This method provides accurate results where there are two obstructions, with one being clearly dominant. However, it tends to over-estimate losses where there is no dominant obstruction. For 3 or 4 obstructions the Deygout method gives the best results of any of the approximate methods. However, for 4 or more obstructions Deygout will tend to overestimate the loss.



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3.4.2

Effective Antenna Height Calculation

An important parameter with respect to the topography is the determination of the effective antenna height. As the mobile station moves, the effective base station antenna height changes. Figure 2 illustrates some of the possibilities as well as the different methods to determine the effective antenna height.

Figure 2: Possibilities to determine the effective antenna height 3.4.2.1 Absolute Method

The absolute method uses the height of the base station antenna above ground as the effective antenna height. The absolute method is suitable in flat terrain. 3.4.2.2 Average Method

The average method is calculated as the base station antenna height above the average terrain height across the area of the prediction. The average method is suitable in flat or gently rolling terrain. 3.4.2.3 Relative Method

The effective antenna height is determined as the relative height of the base station antenna to the mobile station, if the height above sea-level of the mobile station is lower than the height above sealevel of the base station antenna. In the reverse case, the height of the base station antenna above ground is the effective antenna height. Otherwise this definition leads to negative antenna heights. The relative method is reliable in rolling hilly terrain where the mobile station is mainly below the base station antenna.



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3.4.2.4

Slope Method

The effective antenna height is defined as the line from the base station antenna to the fictitious elongation of the mean terrain in front of the mobile station towards to the base station. The definition have to restrict by a limitation of the result effective antenna height. Otherwise the slope method can lead to negative antenna height or lead to very height antenna heights (>200m). In conjunction with the Hata-models, the slope method should be restricted to a minimum effective antenna height of 30[m] and a maximum effective antenna height of 200[m]. The slope method is suitable in very wavy areas where the terrain increases or slope very strong in s front of the mobile station.

44.1

Indicators of Prediction Model PerformanceBasic Statistics

The statistics enable the user to assess the accuracy and reliability of the specified propagation prediction model. The in common use statistics are: y y y Mean error Root mean square (RMS) error Standard deviation

The prediction error at a given point i is the result of the measured signal subtracted by the predicted signal level:

?dBm A

The mean (median) prediction error is the sum of the prediction error over all n points: Q 1 ! ?dB A n

i !1

n

Qerr r i ?dB A

The mean prediction error shows the tendency of the specified propagation model. A positive mean prediction error signifies that the prediction model is too pessimistic. Accordingly, a negative value signifies that the prediction model is too optimistic. The root mean square error (RMS) is defined by:

i !1

The RMS declares the overall variation range (mean prediction error and standard deviation) of possible prediction error. Consequently, the RMS error is greater than the standard deviation, if the mean prediction error is unequal 0 [dB]. The RMS error is equal the standard deviation, if the mean prediction is equal 0 [dB]. The standard deviation is a suitable indicator to assess the accuracy of the prediction model and is defined by:



!

Q RMS ! ?dB A

1 n

n

Q err r i 2 ?dB A

Si nal _ measured

Si nal _ r dict d

?dBm A

Q err r i ! ?dB A

i

i

(4.1.1)

(4.1.2)

(4.1.3)

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W ! ?dB A

1 n 1

Q Q err r i ? A ? A dB dB i !1

A low standard deviation in conjunction with a low RMS error indicates a well-tuned prediction model.

A typical statistical result by a well-tuned propagation prediction model is a mean error of r3 [dB] and a standard deviation of 8-9 [dB].

4.2

Prediction Error Statistics of Aircom International Asset

Aircom International Asset provides several features supports the model calibration process. However, not all features suitable for the model calibration. For additional guidance for using the features of the Aircom Asset CW Measurement Analysis tool, please have a look into the application note ASSET Standard Macrocell Model Calibration provided by Aircom International. 4.2.1 Displaying Prediction Error in the 2D -View

Following items can be displayed on the Aircom Asset 2D-View: y y y y Measurement Route Carrier Wave Route tags Carrie Wave Signal Carrier Wave Signal Error



"

n

2

(4.1.4)

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Figure 3: 2D-View displaying Carrier Wave Signal Error

Especially for areas with wavy (hilly) terrain, displaying the Carrier Wave Signal Error together with the Terrain Height can point out graphical the coherence between prediction error and influences of actual terrain along the direct propagation prediction path or terrain features at the location of the MS. This feature is useful to find a suitable effective antenna height calculation method and/or knife-edge diffraction method.

4.2.2

Displaying Received Level/Prediction Error vs. Log(d)

Figure 4 shows the graph Received level vs. Log(d) (similar to Prediction Error vs. Log(d)) produced by Asset. The different colored data points represents different clutter types.



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Figure 4: Asset Graph of Received Level vs. Log(d) By this feature it is impossible to find out specific adjustment coefficients from a extend cloud of points. Furthermore, it is difficult to find out tendencies. 4.2.3 Asset Analyse Text File

Aircom Asset will produce a text file similar to the shown in Figure 5.

File Summary

Overall Summary

Clutter Summary

Figure 5: Asset Analyse Text File

The following table shows options available in the Asset Analyse text file. Options File SummaryAuthor: Doc-ID: Date:

1

#

in Information

Suitable to... point out failed measurements of one test siteRevision: Page: 1.1 15 of 53



2 3 4

Overall Summary Clutter Summary Bin Information

calibrate the intercept calibrate the clutter adjustment import into a spreadsheet application, from where it is possible to produce charts and graphs

Option 13 provide more detailed (numerical) information as against the graph Received Level/Prediction Error vs. Log(d). However, the option give only overall (summary) results and provides no information about the progress of the prediction error and the standard deviation with distance. The analysis of the prediction error with distance is needed to calibrate the slope adjustment coefficients or to determine near and far adjustment coefficients. Option 4 provides the possibility to import the results into a spreadsheet application like Excel. Unfortunately, Excel can only process 32000 lines (equivalent to 32000 bins). In many cases, the number of bins available for analysis will be more than 32000.

4.3

Prediction Error Statistics of MeaAnalyse

The application MeaAnalyse creates several numerical statistics in tabs separated ASCII text file for the import into Excel, from where it is possible to produce charts and graphs. The purpose of MeaAnalyse is to provide statistical outputs allow an easier and faster analysis of the survey data (calibration of the propagation model), for user not involved in the model calibration as well. MeaAnalyse subdivides the survey data included for the CW measurement analysis to the categories LOS&NLOS, LOS and NLOS as well as to the used clutter. Furthermore, MeaAnalyse determine not only the overall mean prediction error, RMS error and standard deviation, but also for user defined segments (e.g. determine the mean error, RMS error and standard deviation in segments of 500m) of distances from the test site(s). MeaAnalyse processes the Aircom Asset Analyse text file contains the bin information (option 4). A guidance how to use MeaAnalyse together with Aircom Asset attached in the Annex. MeaAnalyse creates 6 output ASCII text files: _DistributedBins.txt _DistributedMeanError.txt _DistributedRMSError.txt _DistributedStandardDeviation.txt _DistributedStandardDeviationVsMeanError.txt _Summary.txt The first 5 outputs are spread sheets to import into Excel. The file _Summary.txt can be displayed in a ASCII text editor. For the model calibration process the files _DistributedStandardDeviationVsMeanError.txt and _Summary.txt very useful. For the graphical presentation of the propagation model performance (e.g. mean error with distance) the first 4 outputs very useful.

4.3.1

MeaAnalyse output file _Summary.txt



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The MeaAnalyse output file _Summary.txt provides numerical the prediction error statistics like mean error (MeanErr), RMS error (RMSErr) and the standard deviation (StdDev) as well as the number of bins (Bins) considered for the CW measurement analysis. Figure 6 shows in detail the content of the MeaAnalyse output file _Summary.txt.

Figure 6: MeaAnalyse output file _Summary.txt The survey data (prediction error statistics) subdivided by Clutter Type (e.g. Clutter Type : light_density_residential) included for the CW measurement analysis in Aircom Asset. Each found clutter category subdivided into LOS (receiver (pixel) has line of sight to base station), NLOS (receiver (pixel) has none line of sight to base station) and Total (summary of LOS and NLOS). Furthermore, the prediction error statistics subdivided into distance Intervals (user defined segments). In the example shown in Figure 6 the user defined interval is 250m. In Aircom Asset the CW measurement analysis performed for a radius of 5km. Consequently, the survey data (prediction error statistics) subdivided in 20 segments. Below the intervals, the line starting with Total shows the summary of all bins assigned to this clutter category. At the end of ASCII text file the table below the line Clutter Type : Total shows is the overall statistics.



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4.3.2

MeaAnalyse feature StandardDeviationVsMeanError

The purpose of the MeaAnalyse feature StandardDeviationVsMeanError (graph created by Excel) is to provide a simple proceed to rate the propagation model performance after altering adjustment coefficients or to compare the improvement by using different knife-edge or effective antenna height calculation methods. Figure 7 shows the typical graphical output created by Excel. The chart shows the standard deviation (yaxis) versus the mean prediction error (x-axis). Each ball represents one segment (interval of distance from the test site(s), e.g. 250m). The size of the balls shows graphical the number of the bins assigned to the distance interval. The ball has the size 100% stands for the interval contains (covered by the) highest number of bins.

Figure 7: MeaAnalyse feature StandardDeviationVsMeanError

The closer the balls together and the closer all balls to the origin the more fitted the propagation model to the measurements. The example shown in Figure 7 the effective antenna height calculation method Slope (second chart) gives a better fit to the measurement as against the Absolute method (first chart). Because, in the second chart the balls closer together.



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4.3.3

MeaAnalyse feature(s) ...versus Distance

The overall mean prediction error and the overall standard deviation (in the end two values only) no suitable indicators to judge the quality/reliability of the calibrated path loss prediction model. A mean prediction error of 0 [dB] expresses nothing on that the calibrated prediction model is faultless within the radius of 3km from the test site or that the prediction error is 0 [dB] at distances greater than 10km . For the RF planning (design) process it is important to know the expected mean prediction error and standard deviation (apropos Fade Margin) within the (designed) cell radius. Furthermore, it is important to know the mean prediction error on far distances impacts on the size of the service area and the results of the interference analysis. The Aircom Asset CW Measurement Analysis tool supports no suitable features provide that. Only the Bin information processed in Excel (or other spread sheet applications) allows the user to obtain the information. Unfortunately, Excel can process only 32000 lines correspond to 32000 bins (measured points). In may cases more than 32000 bins available for (included in) the CW measurement analysis. Therefore, MeaAnalyse creates several spread sheets for further easy and uncomplicated process in Excel. The Figures 8 show charts display the number of bins, the mean error, the RMS error as well as the standard deviation versus the distance created from the output MeaAnalyse ASCII text files _DistributedBins.txt, _DistributedMeanError.txt, _DistributedRMSError.txt and _DistributedStandardDeviation.txt. For the examples below the curves show the summary of the clutter types included in the CW measurement analysis. But it is possible to created the charts (statistics) for a single clutter category (e.g. urban) as well.

Figure 8: MeaAnalyse features after imported/processed in Excel

It is recommended to include such charts into the model tuning report. Because, it also provides information about tuned model to people were not involved in the model calibration process.



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55.1

Input DataMap Data

5.1.1 Paper Maps Paper maps (with scales from 1:10.000 to 1:250.000) provide extra geo-information (e.g. distribution and density of the morphology) of the area surrounding the test site and mobile station. Approximate all RF planning supports the import of scanned maps. For many regions or countries, scanned paper maps are available in the World Wide Web. Figure 9 shows the detail a 1:25.000 paper map (source jpeg format) of the area around Entroncamento (Portugal). Figure 10 shows the detail of a 1:50.000 paper map (source jpeg format) of the city Columbia in the USA.

Figure 9: Scanned paper map (scale 1:25.000) of Entroncamento (Portugal)

Figure 10: Scanned paper map (scale 1:50.000) of Columbia City (USA)Author: Doc-ID: Date: H.H. Rhrig Lucent Technologies Proprietary RFET-QA-REP-00-010-V01.00 12 September 00 Use Pursuant to Company Instructions Revision: Page: 1.1 20 of 53


5.1.2

Topographical Database

The topographical database is the integral component in the coverage analysis. The DTM (digital terrain model) data base provides topographic concerning location and shape of obstacle information (terrain features like hills, mountains or valleys) impacts on wave propagation. The clutter database revealing geographic distribution of natural (e.g. open, water forest) and built-up features (man made like urban, suburban, village) impact on wave propagation. Typical DTM and the land usage information (clutter) are structured by raster data. A raster element (pixel) represents the average terrain height above sea level or a clutter code for a square area of, for example, 100m x 100m. Terrain databases are created from topographical paper maps and/or from satellite images and are available in different types of resolution and number of clutter categories. The typical used terrain databases are: y Countrywide data sets are recommended for Macrocell coverage analysis in rural and semi -rural environments. The database almost created from 1:100.000 to 250.000 topogra phical paper maps, depending on country. Typical is a 5-class land usage map. The typical resolution is from 50mx50m up to 100mx100m. The planimetric accuracy is (x,y)=100m by 1:200.000 (50m by 1:100.000). The altimetric accuracy (z) is from 20m up to 40m by 1:200.000 (10 to 20m by 1:100.000), depending on the relief. City packages or urban data sets should be used for the coverage analysis in towns and cities. The typical resolution is 20mx20m. The DTM created by digitization of 1:50.000 scale topographic al maps. The clutter information based on 10m resolution Spot satellite imagery. Typical is a 15-class land usage map. The planimetric accuracy is (x,y): 20m. The altimetric accuracy (z) is from 7m up to 12m.

y

Keep in mind, that: y y y y y 100-meter clutter databases are created from topographical paper maps that are older than 3 years (depending on the country) usually. Thus, the land usage information is not up-to-date. Satellite images guarantee up-to-date land usage information. Clutter databases created by a satellite image do not guarantee reliable clutter information. The clutter database is the interpretation of a person, who analyzes groups or cluster of gray -pattern in the image and try to assign to this group the most likely suitable clutter category. The resolution of the terrain database is not a guarantee for the quality of the database Terrain databases with more than 20 clutter classes (recommended are 15 -clutter classes) can provide extra geo information for the RF planer (e.g. traffic distribution) and perhaps improve the coverage analysis. However, it requires more survey data (see RFGuidelineCWMeasurements.doc) and complicates the model tuning process. In city packages, the data supplier offers extra clutter categories like MainRoads or OpenInUrban. These classes are created by line data information. All pixels are covered by the line data represents Roads or Streets will be assigned to the clutter category OpenInUrban. In case of tuning empirical models like COST231-Hata, do not insist on this extra geo information, if possible. Experience has shown that it complicates the tuning process and it didnt improve noticeable the coverage analysis. Furthermore, clutter categories like urban or suburban already characterize the local probability of building density and roads in a certain area.

y

To avoid problems performing the model tuning process: y Check the real planimetric accuracy by paper maps or by the survey routes. If the shift is greater than the declared planimetric accuracy, then in worst case the survey routes cover the wrong clutter areas.



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y

Check the clutter assignment by high-resolution paper maps or by the local knowledge of the country project teams.

5.2

CW Survey Data

CW field strength measurements are necessary in order to tune an empirical propagation model. Increasing the number of survey data improves the reliability of the tuned propagation model. However, a tuned propagation model is only good as the quality of the input CW survey data used to calibrate it. To collect a statistically significant amount of accurate survey data take into account the subjects as follows: y y GPS, D-GPS or any other kind of positioning system should geographically reference the data points along the survey route. To fulfil the sampling theorem with respect to the Doppler shift (i.e. record at least two impulse responses per wavelength), the field strength should be measured every half wavelength ( /2) by triggering by the survey vehicle (e.g. wheel triggering). To get of the long term fading, fast fluctuations have to be filtered out. This is done by averaging the survey data over a gliding window with a minimum length of 40 (Lee-criteria). The maximum length is 200 . To achieve a representative set of data collection the number of test site locations should be at least of 10. The test sites locations should be representative of the sites in the planned or running mobile network (antenna heights and environment surrounding the test site). The test site antenna height depends on the average operating antenna heights of the RF network in the future. For Microcells (or UMTS), the test site antenna height should be 12,5m and 25m. For Macrocells the test site antenna height should be 25m and 40m. For the different test site antenna heights, the same measurement routes have to be driven. For measurements within Macrocells the minimum length for one survey route should be between 200 km and 300 km. For CW measurements within Microcells the minimum length for one survey route should be 100km The survey data should be evenly distributed with respect to distance from the test site and distributed with respect to the clutter categories that are used in the topographical database. After averaging the survey data a minimum of 300 data points per 1000m-distance interval and per clutter category is recommended. Example: For the calibration of a prediction model for suburban application and a planned prediction radius of 10km, the minimum number of overall measured (and averaged points) for the clutter category suburban should be 300 *(10000/1000) = 3000 points. Therefore, it is important to plan the survey routes with help of digitized terrain database, especially the land usage database. The survey routes should be zigzag or stair routes to avoid street direction propagation relative to the test site especially for urban (build up) environments and dense vegetation areas. Indeed, guiding effects in dense urban street canyons with LOS may lead to path loss values which are up to 4 dB lower than under normal inner-city propagation conditions. Avoid surveys on short-term conditions that could impact on wave propagation like peak ours or the weather (thunderstorm or snow fall) and the wetness of surroundings after rainfall. Furthermore, keep in mind the impact on wave propagation on the seasons (deciduous forest in winter).

y

y

y

y

y

y

y

A further guidance is given in the word document RFGuidelineCWMeasurements.doc.Author: Doc-ID: Date: H.H. Rhrig Lucent Technologies Proprietary RFET-QA-REP-00-010-V01.00 12 September 00 Use Pursuant to Company Instructions Revision: Page: 1.1 22 of 53


5.35.3.1

Start Parameter Values of Propagation ModelAircom ASSET Standard Macrocell Model

Aircom International ASSET supports a Standard Macrocell model based on the Okumura-Hata and/or the COST-231-Hata model. These classical field-strength prediction models were developed for large radio cells, i.e. radio paths larger than 1km. Furthermore, the models applied to large base station antenna heights, i.e. the base antenna is installed considerable over the rooftops of the surrounding buildings. The Aircom ASSET Standard Macrocell Model is defined as follows: L(d) = k 1 + k 2lg(d) + k 3(Hms) + k 4lg(Hms) + k 5lg(Heff) + k 6lg(Heff)lg(d) + k 7Ldiffn + C_Loss (5.3.1)

Where: d Hms Heff Ldiffn k1 k2 k3 , k 4 k5 k6 k7 C_Loss distance from the base station to the mobile station [km] height of the mobile station above ground [m]. effective base station antenna height [m] diffraction loss calculated using either Epstein Peterson or Deygout intercept, corresponds to a constant offset slope adjustment coefficient correction factor used to take into account the effective mobile antenna height Effective antenna height gain. This is the multiplying factor for the log of the effective antenna height LOG10(Heff) is the multiplying factor for LOG 10(Heff)log(d) multiplying factor for the determined diffraction loss clutter adjustment coefficient

Aircom Asset supports the knife-edge diffraction methods y y y y Epstein-Peterson Bullington Deygout Japanese-Atlas

Aircom Asset supports the effective antenna height calculation methods y y y y Absolute Average Relative Slope



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5.3.2

Classification of Hata Adjustment Coefficients to ASSET k -parameter

Table 2 shows the classification of the Okumura-Hata/COST-231-Hata adjustment coefficients to the kparameters of the Aircom ASSET Standard Macrocell Model. The Okumura-Hata and COST-231-Hata adjustment coefficients obtained from the equations (3.1.1) and (3.2.1).Aircom ASSET k-parameter Okumura-Hata Model150 MHz < f

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