Model Tuning Concepts  TornadoN
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Propagation models and Model Tuning with TornadoN.....................................................2 1 Introduction............................................................................................................................2 2 Radio propagation formulas applied within TORNADO..................................................3 2.1 The OkumuraHata Formula (COST231) for Macroscenarios....................................3 3 Model Tuning concepts for Macrocells................................................................................6 3.1 Model Calibration...............................................................................................................9 3.1.1 “ThroughClutter loss” in TornadoN.........................................................................................................14
4 Experienced Models and Results (Macrocells)..................................................................16 4.1 K1K6 factors....................................................................................................................16 4.1.1 Clutter parameters......................................................................................................................................17 4.1.2 Else
19
Siemens AG Information and Communication Mobile Networks
Model Tuning Concepts TornadoN (macro)
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Propagation models and Model Tuning with TornadoN 1 Introduction This document has been completely orientated on the previous Model Tuning Concepts for the former Tornado (Planet – MSI). It has been found that Model Calibration process can exactly be interpreted as the same, where main efforts were seen in the transformation of the already found standard model parameters. Since their values and algorithms are correlated to initial rich experience with Tornado, further new algorithms and parameters in TornadoN have to be carefully and consciously applied. However enough information is provided for a successful empirical propagation model evaluation with the right/sufficient set of usable/practicable parameters and algorithms. The propagation models are the main aim to adapt radio attenuation in the real field. The tuning of its certain parameters can classify different environments like urban or rural areas. Therefore, different statistical formulas are existing in order to investigate appropriate scenarios with a high reliability. In this document the tuning process shall be analysed on macro (TxAntenna height above roof) buildup scenarios. Finally detailed concepts for model tuning, resulting in reliable radio predictions, are given. The following pages are explaining the most known and practicable radio propagation formulas for buildup area, OkumuraHata formula for macro environments. Additionally their implementations within TornadoN (Aircom Enterprise – ASSET) and the parameters correspondence are clarified. Chapter three concerns with concepts for tuning the mentioned models. The last chapter four indicates the main factors of experienced models and therefore can recommend appropriate models to certain scenarios, including its starting values. It should be noticed that the used terrain databases have the recommended typical raster resolutions for macro cell, 20m for urban areas, 50m, 100m (depending on the roughness of the regional surface) for regional areas, and for micro cells 5m. Since the OkumuraHata formula is a pure statistical approach for predictions of radio propagation behaviour, the usage of finer (macro scenario) raster resolutions for their certain application areas are not justified by the propagation models.
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2 Radio propagation formulas applied within TORNADO 2.1
The OkumuraHata Formula (COST231) for Macroscenarios
The Hata radio propagation formula is based on the Okumura measurements in the urban area of Tokyo at frequencies of up to a maximum of 1500 MHz. This formula applies to flat urban areas. Correction terms are specified by Hata for suburban and open areas and are represented by L(Clutter). Formula valid for frequencies between 150 – 1500 MHz hBS h f d L pathloss = 69 .55 + 26 .16 log − a MS + s * log − 13.82 log + L(clutter ) km MHz m m h s = 44 .9 − 6.55 log BS m The formula had to be modified slightly to remain applicable in the frequency range of 1800MHz. The result of this modification was the COST231 Hata model. Formula valid for frequencies between 1500 – 2000 MHz hBS h f d L pathloss = 46 .3 + 33 .9 log − a MS + s * log − 13 .82 log + L(clutter ) km MHz m m h s = 44 .9 − 6.55 log BS m
Common validation (150MHz < f < 2000 MHz): Base Station Height hBS : Mobile Height hMS: Distance d :
30 – 200 m 1 – 10 m 1 – 20 km
These two approaches form a usable basis for most 900 and 1800 applications on level terrain. The pathloss coefficients of the Hata model have been converted for a flexible use within the TornadoN “Standard Macrocell” model. Changes to the basic parameters for these equations should be kept to a minimum. The general equation of a statistical prediction model in TornadoN, interpreted as a transformed Hata model, is as following. Note that in comparison to the old Tornado, distance Siemens AG Information and Communication Mobile Networks
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unit in km (before, meters) and pathloss level (before, signal power strength) are taken for prediction calculation. PATHLOSS (dB) = Ptx – PrX = K1 + K2log(d) + K3(Hms) + K4log(Hms) + K5log(Heff) + K6log(Heff)log(d) + K7Diffr + Lclutter where : PRX
=
measured receiving power (dBm)
PTX
=
transmitting power EIRP (dBm)
K1
=
constant pathloss offset, comprehensive of the term log(frequency) (dB)
K2
=
multiplying factor for log(d); slope
K3
=
OkumuraHata correction factor for the effective mobile height
K4
=
multiplying factor for log(Hms) – compensates for gain due to mobile height
K5
=
OkumuraHata type of multiplying factor for log(Heff) – compensates for gain due to antenna height
K6
=
OkumuraHata type of multiplying factor for log(Heff)log(d)
K7
=
multiplying factor for diffraction calculation
Kclutter =
clutter correction factor (dB)
d
=
Tx – Rx distance (km)
Heff
=
base station effective antenna height (m)
Hms
=
mobile height (m)
Diffr =
diffraction loss (dB)
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There is no frequencydependent term in the TornadoN “Standard Macrocell” model, it is taken into account in the fixed factor K1 of the standard model. In the plain Hata equation, diffraction effects are not handled and the antenna height of the base station is considered to have a static size. In the TornadoN models, both effects can be handled dynamically (even by choice of different algorithms); the terrain profile saved in the database is then taken into account. The losses due to obstruction by terrain obstacles are determined by extension of the model for knifeedge diffraction. They can be weighed with a selectable factor K7. Multiple diffraction caused by adjacent obstacles would produce high overall attenuation. This effect can be limited by merging several obstacles within a selectable distance. K(clutter) is called clutter gain and represents a fixed power correction for environments which differ from the one assumed by Hata situated in Tokyo (urban). The clutter gain has to be defined for each clutter that exists in the terrain database. This value is treated as the GAIN of the individual clutter class with respect to the reference of TokyoCity. Obviously, the model can be referred to a specific clutter, whose clutter gain will be put at zero, while each other clutter correction factor will be estimated with respect to it. If the original Hataformula COST231 respectively is compared with the in TORNADO implemented standard model, the following terms can be corresponded to the different Kfactors (without K4, K7). f d h h d h L pathloss = 69.55 + 26.16 log + 44.9 log − 13.82 log BS − 6.55 log BS * log − a MS + L(clutter ) MHz km m m km m
K1
K2
K5
K6
K3
K(Clutter)
NOTE: With respect to the changed distanceunit within TorndaoN which is kilometer (km), the formula has NOT to be converted. Previously in the old Tornado, additional terms regarding the K1 and K5 factor had to be included.
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3 Model Tuning concepts for Macrocells The TornadoN tools offer several approaches to building prediction models for the signal level and for the field strength distribution to be expected in a planning area. This guide is limited to the Standard Macrocell Models which are the most manageable methods for the planner, and which can be used in practice in all occurring scenarios. This description does not cover all of the options for model calibration, especially offered in the Standard Model. The various possible presentation methods may have effects which are to a certain extent contrary or partially counteractive or accumulative. These dependencies are described here. When modelling is practised on a multilevel basis involving the simultaneous use of several parameters with different effects on the prediction (e.g. simultaneous use of "clutter factor" and "clutter height"), the overall behaviour of the model quickly becomes difficult to assess. All terrain have particular features with regard to radio attenuation. It is recommended, that an area visit/survey is proceeded by the engineer responsible for adjustments of the prediction model, even though radio measurements are available. While tuning, the engineer will also remember the peculiarities of the area and be in a better position to explain any deviations in the measured results. Radio planners should avoid creating models which are so complex that their behaviour is difficult or impossible to follow. It is a fallacy to assume that the quality of a prediction increases with the number of tuning parameters. Although it is certainly possible to emulate the measured result of a specific survey with great precision by activating several variables, it should not be overlooked that the aim of the model calibration is not to reproduce individual measurement routes as exactly as possible, but to elaborate the overall radio characteristics of an area. The resulting model should retain its informative value in other, comparable areas for which no measurements are available. This goal is best achieved by evaluating as many surveys which are typical of the area as possible and hence can be reused with constant high accuracy.
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When looking at the transformed Hataformula implemented as the Standard Macrocell model in TornadoN, the parameters can be distinguished between the original ones and the extended ones. The original ones K1, K2, K3, K5 and K6, investigated by Hata and the COST231 should differ slightly by tuning a model in order not to decrease the reliability of the origin formula. One extension of the Hataformula concerns heavy signal fluctuations near the site, caused by LOS and NLOS paths, make the average field strength rise over the values predicted by models tuned for higher distances, where a lower occurrence of LOS paths makes signal variability be more restrained. “Twopiece” models were defined to take into account this effect; the global curve is composed by two pieces with different slopes (signal VS distance), joined at a certain distance (generally 1 km, that is the lower limit for Hata model validity). A plausible approach to modeling the near section would be to accept the free space loss at a distance of 20m and the attenuation calculated with the Hata formula at a distance of 1 km. This results in the following slope within the near field up to a transition break point dBP=1km.:
slope 2near = ( Lpathloss (d = dBP) − LFSL (d = 0,020km) ) / ( log(dBP) − log(0.020km) ) K 2near = slope 2near + 6,55 * log(hBS ) NOTE: When designing a 2piece model, the planner must make sure that there is no attenuation step at the transition point. The K1n factor must therefore be adapted accordingly.
K1near = K1far + ( K2far  K2near )*log dBP For a transition point of 1km, therefore:
K1near = K1far
PathLoss
K2far K1far K2near K1near
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Clutter correction factors (Kclutter) were introduced in order to adapt Hata equation to each morphological class, taking into account the different effect of every kind of environment for what concerns propagation; practically speaking, this idea allows to use the same algorithm in every environment (urban or rural), simply shifting the basic curve to fit signal behaviour for every land usage class (urban, open area, forest, sea, etc.). Another approach to describe the propagation area is, to use for each clutter class the height and separation parameter INSTEAD of its correction factor. This could be very accurate especially in urban areas where diffraction is the dominant attenuation path. As clutter heights extends the terrain height and is determining the obstruction loss, the clutter separation factor separates the mobile from the surrounding clutters and hence prevents from high losses by adjusting the heights for use in the diffraction calculation (K7*D). In this context a lower K1factor is needed to decrease the propagation curve with the higher diffraction loss which is especially excessive in the near field. Note: With the introduction of clutter heights, the propagation model becomes a more deterministic method in comparison to the pure empirical OkumuraHata formula. Originally no diffraction losses were involved and only two statistical clutter classes (suburban and open) were evaluated. A decision between these two clutter tuningconcepts shall be made in accordance to the following aspect. The height and separation concept have to be seen as an alternative, since the most experience is existing on tuning the correction factors which can be applied on similar environments. The main background is that the tuning of heights and separation parameters requires more detailed and accurate/reliable terrain data (height and clutter) to guaranty slight deviations between the average parameters of each clutter and the realistic measures of the dominant obstacles within a clutter area. A high resolution terrain database like 20m for urban areas, results in a lot of morphological clutter information. Hence if models of these two concepts are compared, the model tuned by the height and separation clutter parameters results in a less prediction error of course, but corresponds only to the certain clutter area and finally can not probably be applied on similar clutter scenarios. However in case the coverage area is affected by morphological obstacles situated in a clutter class between transmitter and receiver, the tuning of the clutter factors can not take into account such propagation characteristic. Even if the path clutter algorithm is enabled, the investigated parameters of path clutter would only be reliable if a lot of measurement data have been evaluated. The diffraction algorithm on the other hand also includes obstruction losses from each morphological obstacle encountered on the propagation path, provided that the height factor is specified on every clutter class. The terrain profile (topography) can also be specified as itself on a model for a certain area. It is considered on the path loss calculation by evaluating the effective antenna height between transmitter and receiver. There are several algorithms in the standard model for determining the effective antenna height. In each case the bestsuited method must be determined from the prevailing terrain profile. If only one specific road needs to be covered in hilly terrain, the choice may be different to that of a whole area. Siemens AG Information and Communication Mobile Networks
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3.1
Model Calibration
ATTENTION: The model to be tuned in TornadoN is that one named standard macrocell model 3: user reference guide strongly recommends to use it, because it performs more accurate antenna masking, whereas models 1 and 2 are included only for cases when upgrading and retuning are not desired. The receive signal level reveals a long term fading characteristic with normal distribution, even in areas with a very homogeneous building character. The typical standard deviation for this spread is approx. 7dB in urban areas. The level predicted with TornadoN is the mean level of the signal. The statistic evaluation of the prediction error with TornadoN generates the mean deviation (mean error = predicted path loss  measured pathloss)) and the quadratic mean of the error (RMS value). There is no point in attempting to use all available means to force the RMS value under the value of the typical standard deviation. In practical terms, a RMS error of around 8dB can be considered as a very good tuning result. For the mean error you should aim for a slightly positive value (approx. +3dB...+2dB). This would provide for a slight safety margin in the predicted “pessimistic” value. A very important parameter is the error regression curve slope, because the closer to zero it is the more faithfully it simulates the actual field strength decay with distance from site. The improve of coherence between the model slope and the actual power attenuation with distance from radio base station let it be possible to deduce cell dimensions with little error margin and, as a consequence, the number of predicted cells necessary to cover an area will be more reliable. A slight positive sign of the error regression curve slope (max +10 dB) also refers to a healthy pessimistic approach in order to give a higher probability in overlapping cells than being below the predicted signal (at a lower probability) resulting in coverage gaps. Model performance analysis, when applying 2piece model feature, shall consequently be seen before (near) and after (far) transition point. Their application area, nearfield as the coverage representative and farfield as the interferer representative, then can individually be evaluated. The following picture of model and measurement data analysis has been taken from the visual feature in former Tornado Survey Tool. Since here, received signal strengths are compared (predicted – measured) inverted signs on the model performance indicators (regression slope, mean value of error, both with negative values) were main target for a slight pessimistic propagation model.
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Error Regression +RMS Mean Error RMS
Standard Deviation
Signal Regression = Slope
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The experienced calibration procedure in context to the clutter factor concept is summarised below. Afterwards hints for tuning the clutter height and separation parameter are given in order to indicate an initial sign of its calibration procedure. Finally a lookup table figures pros and cons of these two concepts and their application areas. 
Call up the survey tool, select a survey station

Check whether the data recorded in the survey header are correct (antenna type/height, EIRP, etc.)

Select the start model best suited to the relevant area

Select a measurement route and compare with the prediction

Eliminate the uninformative measurement points A good model must be based on a big number of measurement runs. This is the only way of succeeding in establishing the radio type of the builtup area or the existing vegetation. This clutter type should then be classified by assigning suitable clutter factors. Individual measurement results which deviate considerably from the normal spread should not be taken into account for the calibration if the reason for their deviation is recognisable (e.g. underpass). They should not affect the model parameters. All measured values which are not at a particular distance from the sensitivity limit of the receiver must also be eliminated, since otherwise the signal level disappears in noise. For CW measurements with a bandwidth of 10kHz results for a certain receiver system in a limited receive power level of 124 dBm. A mean output level of 115dBm then still reserves sufficient margin to recognize the lower drops of Rayleigh fading.

Now delete the obstructed zones: (delete obstructed) Obstruction is considered to be caused by topographic obstacles only but not by buildings or vegetation. The survey analysis can now be used to determine whether the prediction has particular shortcomings for certain clutter categories  a change to the affected clutter factor Kclutter can bring sufficient improvement here  or whether a distancerelated error can be found (slope error). The error and the regression lines of all the measurement points can be presented with respect to distance using the function “Graph” “log(d) vs. error”. These evaluations can be supported by, for example, deleting individual categories or by limiting the evaluation range (delete close in/far out) or excluding excessive high or low levels (delete low/high signal). The presentation of the error along the route (draw residuals) is also helpful. If a satisfactory result cannot be achieved using clutter changes and/or slight K1/K2 changes, it is still possible to choose a different method of determining the effective antenna height. For example, if the error remains constant over several clutter areas without a distance dependency, this may indicate that the setting for "effective antenna height" has not been the best choice

Now delete only the nonobstructed measurement points: (delete LOS) Consider only the obstructed points and optimize the K7 diffraction factor.
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Remove deletion of LOS and NLOS points: Check the analysis result for the entire measurement route (except for the dubious route sections or where the recorded level is drowned in the receiver noise)

Carry out further surveys and check whether the chosen parameters are confirmed. This will not necessarily be the case for complicated terrains ! You will then have to find a compromise.

Finally: overall analysis of all relevant measurement journeys in the area of coverage of the measurement station.

Good luck!
Calibration procedure for clutter heights and separation concept slightly changes in Model tuning because of extending terrain height with clutter height and its inclusion together with the separation parameter to calculate the diffraction loss. In this context it is really recommended to accurately investigate the environment for average clutter height assignments. The model tuning of an area with several clutter classes have to be seen as an iterative process, since changes of each clutter parameters on the propagation path influence the diffraction calculation. As clutter height parameter is representing the real coverage area, the clutter separation is the tuning parameter which can characterise the height distribution within a clutter class. The option in the survey tool to distinguish between LOS and NLOSmeasurement data now involves the clutter height. However when evaluating LOSdata (delete obstructed) fine tuning of K2factor shall be done at the end, since the K1factor definitely changes when tuning the parameters for diffraction loss of the obstructed data. The important steps of model calibration using clutter height and separation concept can be seen as follows. (Replaces the certain steps (italic script) of the model calibration with clutter factors) 
In the start model, average clutter heights and clutter separation have to be set instead of clutter factors (set to zero!).

Consider NLOS data (delete nonobstructed) K7 Tuning and K1Alignment; changes of several clutter separation in accordance to the median error of the certain clutter class. This will result in adapting its impact on separation parameters of following (away from Tx) clutter classes and the K1factor. Afterwards K4Tuning (K1Adaption) with respect to error regression curve.

Consider LOS data (delete obstructed) Slight tuning of K1/K2factor

For finetuning the mentioned process (bold steps) perhaps have to be repeated
NOTE: Due to the fact that K1 definitely raises/lowers predicted path loss, K1key values shall be found for certain building environments. Evaluated clutter diffraction characteristics (height and separation) shall be analysed to achieve experience for its application on similar building structures or clutter classes or even build up scenarios. Siemens AG Released: ICM N MS P6 Information and Communication Mobile Model Tuning Concepts Review: Runge / Gomolka Networks TornadoN (macro) Date: Author:
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The lower table summarises pros and cons of the two model tuning concepts. Additionally their recommended application fields are also mentioned. Clutter correction factor
Clutter height and separation
 Experienced model with high  More impact parameters on degree of reliability for similar tuning of diffraction loss clutter areas  Considers clutter impacts  Typical values for different within the propagation path clutter classes  Slight adaptation (offset shift) on origin Hata (COST231) formula  Does not require high detailed morphological data
PROS
 mostly unwieldy and not very  Requires more detailed and accurate , especially in built up reliable morphological data areas where diffraction is the with high accuracy dominant loss  Less experienced models  High efforts and less and no typical values for experience of clutter impacts different clutter classes within the propagation path  Investigated models can not easily be assigned on similar environments
CONS
for reliable prediction with limited accuracy where models can be applied on areas without given measurements
USAGE
• •
for accurate prediction of individual urban areas where diffraction can be seen as the dominant propagation path
Rural and flat homogenous • urban areas Low resolution terrain data
•
Siemens AG Information and Communication Mobile Networks
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High resolution terrain data whereas its clutter classes can be related to its morphological height (average ≈ maximum) Microcellscenarios where antenna is situated above rooftop
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3.1.1 “ThroughClutter loss” in TornadoN The “ThroughClutter loss” in TornadoN option allows to take account of clutter not only in the predicted pixel, but also in those pixels interposed between the transmitter and the receiver. The clutter loss for a predicted pixel is obtained by adding to the clutter offset (offset loss) the loss due to the clutter lying between the base station and the mobile station (ThroughClutter loss in dB/km), included inside a certain distance starting from the predicted point (this distance is named dthrough in TornadoN): a variable weight is associated, in TornadoN, to the clutter lying along dthrough, linearly distributed from 1 (the predicted point) to 0 (the point at dthrough distance from the predicted one), whereas Tornado old allows to define more weight distribution laws. The following equation shows the algorithm for calculating the total clutter loss with throughclutter feature in TornadoN: Total Loss = Offset Loss +
N
∑ i =1
TCLoss i i ⋅ K N
Loss in dtthrough
TCLoss i = Through_clutter Loss for ith clutter K=
1 km pixel size d through pixel size
d through ≠ 0
N= 1
d through = 0
The offset loss, in Tornado old approach, is conceived as the loss due to a certain clutter when no other clutter is interposed between the transmitter and the receiver: path clutter option is used in order to take account of effect of interposed different environment classes, but this effect is null if the interposed clutter is the same as in the prediction pixel. On the contrary, in TornadoN, the throughclutter loss has effect even if the same clutter is present along dthrough; so in order to maintain a behaviour similar to that one implemented in Tornado old (more logical), if the throughclutter option is enabled, it is recommended to set the throughclutter loss equal to the offset loss, and than to modify the offset loss by subtracting from it the throughclutter loss calculated along dthrough (the part of the previous equation inside the frame). Obviously, when throughclutter option is disabled, the original configuration must be resumed (offset loss must be set equal to its original calibrated value, and dthrough and throughclutter loss must be set to zero). Siemens AG Information and Communication Mobile Networks
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Example: 1 2 Tx
3 4 Rx
dthrough (4 pixels)
Let’s suppose the following:  original offset loss = 10 dB (the same for all pixels in dthrough)  pixel size = 50 m  K = 1Km/pixel size = 20  dthrough= 200m (4 pixels)  N = dthrough/pixel size = 4  i = current pixel  i/N = weight of i th pixel contribution to final throughclutter loss Recommended operations: 1) throughclutter loss = 10 dB (equal to the offset loss) 2) new offset loss = 10 – [(10/K)*(1/N) + (10/K)*(2/N) + (10/K)*(3/N) + (10/K)*(4/N)] = 8.75 dB Why this? Because in this way, if path clutter is enabled both in Tornado and in TornadoN, the same final clutter loss (offset + throughclutter) is obtained when one single clutter is found along dthrough.
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4 Experienced Models and Results (Macrocells) The following pages concern with experienced models which contain evaluated and even practicable values for each parameter of the macro propagation models described before. These should help the planner to have first startup values for the initial process of tuning a model and therefore definitely shorten the model tuning progress. Again it is strictly recommended that the practicable value ranges shall be kept in order to gain reliable model in context with radio planning aspects and finally not to reach the lowest prediction error by mathematical sophisticated methods. 4.1
K1K6 factors
The K1factor for 1800 MHz is extended by additional 3 dB with respect to COST231 recommendation for metropolitan areas. The K5 and K6 parameters, which represent the weighting of the BS antenna height and its effect on the slope in the Hata formula, should be changed as little as possible, since it is otherwise difficult to apply the model to other areas. K7factor is only expected a percentage of the diffraction loss, since diffraction algorithm is EpsteinPeterson which calculates high losses. Initial test results indicate K3, K4 factor for pathloss influence of the mobile antenna height are advisably set to zero. The near field parameter K2n (and K1n respectively) is height dependent where the value changes with different BTS height. It is not practicable to have a different model for each Site and therefore these parameters correspond to a reference height of 35m. The following table contains of all Kfactors applying on 900 and 1800 startup models. Find before the parameters conversion rule from former Tornado to TornadoN : Tornado K1T K2T K3T K4T K5T K6T K7T
TornadoN K1TN =  (K1T + 3*K2T) K2TN =  K2T K3TN =  K6T K4TN = 0 K5TN =  (K3T + 3*K5T) K6TN =  K5T K7TN = K4T
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Basic Models for TornadoN 900 MHz
1800 MHz
1piece 2piece 1piece 2piece K1near 146.9 146.9 159.7 159.7 K2near 44.9 50.1 44.9 54.1 K1far 146.9 159.7 K2far 44.9 44.9 K3 0 0 K4 0 0 K5 13.82 13.82 K6 6.55 6.55 K7 0.5...0.7* 0.5..0.3* * values found in former Tornado which can not be confirmed for TornadoN due to changed calculation criteria 4.1.1 Clutter parameters Hata specifies correction functions for the clutter types “suburban” and “open area”. Based on the basic equations listed in 3.1, these have the following values in dB: 900 MHz 10 28
suburban open area
1800 MHz 15 35
Because of the very approximate graduation and the limitation to 2 categories, these can only be typical values. Even the basic value Urban = 0dB can apply only to a very specific building development type (in this case, Tokyo). In principle, all intermediate values are possible, depending on the surface and building structures. In Central Europe values of 0 to around 12dB are feasible for urban areas. The prediction results in clutter types “open area” should undergo a critical verification. The related level supplement would in regions closer than about 500m from the base station predict a path loss lower than free space attenuation The following table provides the experienced values/ranges for typical clutter classifications for 900MHz and 1800MHz. The clutter coefficients conversion rule for TornadoN is the following: KcTN =  KcT TornadoN: Example Dense urban
>10 floors, narrow streets
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900 MHz 0
0
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Page 17 of 20 Page(s)
(type Tokyo) Urban
6...7 floors, streets app. 20m (type Munich centre)
3
3
Suburban
4...5 floors (medium wide streets) 3...4 floors (open suburbs)
(8..9)
(11..15)
(9..11)
(13..17)
Industrial
(heavy buildings) (open type, light buildings)
(8..10) (13..15)
(12..15) (17..20)
Village
open type, Bavarian
(14..15)
(18..20)
Forest
Tropical rain forest, 30m
(5..8)
(6..10)
Dense deciduous forest
(10..12)
(13..15)
Light pine forest
(15..17)
(18..20)
Quasi open
Rural, single houses
(18..22)
(20..25)
Open
(Heath)
(23..25)
(25..30)
(Sahara)
28
35
Sea, Lake
28
35
Water
In case clutter heights are used, “clutter factors” have to be set to zero. Regarding clutter separation, the value shall be taken equal to half the average street width in the corresponding clutter class; in this way, the nearest obstacle (the first building encountered looking from the receiver towards the transmitting site) is that located on the side of the street itself. So, no obstructing edge is discarded, and an accurate description of diffraction can be obtained.
Siemens AG Information and Communication Mobile Networks
Model Tuning Concepts TornadoN (macro)
Released: ICM N MS P6 Review: Runge / Gomolka Date:
Author:
J.Gomolka / D. Boera
20.02.2013
Page 18 of 20 Page(s)
4.1.2 Else Through Clutter Loss Please notice the following list of differences between throughclutter feature in TornadoN and path clutter feature in Tornado: Through Clutter Loss Distance : 300m If equal to Tornado Path Clutter, to be set for each clutter class: Through Clutter Loss = Clutter Offset Loss (new Clutter Offset Loss adjusted acc. to 3.1.1 !!) Path clutter function: triangular (the only one defined in TornadoN) For disabling set: Through Clutter Loss Distance = Through Clutter Loss = 0 Diffraction Algorithm The following table shows the algorithms for diffraction calculation given in TornadoN, a comparison with former Tornado is useful. The EpsteinPeterson method shall be the standard set algorithm for diffraction calculation.
Diffraction algorithms
Enterprise
Tornado
YES YES YES YES
YES 
EpsteinPeterson method Deygout method Bullington method Japanese Atlas method
ATTENTION: There is a discrepancy between the diffraction loss calculated by the Profile feature and the value mapped in the coverage prediction: the Profile feature shows the correct diffraction loss computed by the application of the algorithm reported at page B10 of ASSET User Reference Guide, whereas for the mapped value the model doesn’t consider diffraction losses unless obstacles block the line of sight. In other words, in the coverage prediction the diffraction loss is taken into account only if at least half Fresnel zone is obstructed, otherwise the diffraction loss is set at zero ! Furthermore, concentric rings due to Earth curvature are created: this effect takes place when large prediction is calculated on totally flat (or with very low slope) terrain, because the terrain profile algorithm rounds heights to the nearest meter, producing a profile that falls away from the Tx (because of Earth curvature) in little steps rather than smoothly. A knife edge is created whenever the mobile is behind one of these little steps. Siemens AG Information and Communication Mobile Networks
Model Tuning Concepts TornadoN (macro)
Released: ICM N MS P6 Review: Runge / Gomolka Date:
Author:
J.Gomolka / D. Boera
20.02.2013
Page 19 of 20 Page(s)
Effective Antenna Algorithm Several algorithms are given in Enterprise for estimating the effective base station antenna height. The following table shows a comparison between the algorithms of TornadoN and former Tornado. “Absolute” shall be set as default algorithm.
Enterprise
Tornado
Absolute
Base
Relative
Spot
Average
Average
Slope
Slope
Comments No difference in the algorithm Same results Different algorithms Different results 1 No difference in the algorithm Same results Information from Aircom 2 No difference in the algorithm Different results In Enterprise prediction map, concentric rings appear 3
1
Tornado does not apply the algorithm Spot described in its user reference guide (which neglects mobile antenna height), but the algorithm Relative described in TornadoN manual (which considers also mobile antenna height). On the contrary, Enterprise does not apply the algorithm Relative described in its reference guide, but the algorithm Spot described in Tornado manual.
2
If Heff < 1 then log(Heff) = 0.
3
The different prediction and the effect of concentric rings is due to the same reason described for diffraction.
ATTENTION: for large prediction, also Earth curvature is considered as increasing Heff. If the “Slope height” method is selected, it should be noted that the terrain profile has a strong effect on the effective height determined for each area element. Extreme values must be avoided by choosing suitable limits (e.g. 30m...300m). The choice of the correct “slope distance ds” is also very critical (approx. 1000m ). Great care is particularly important if the model is to be used in other areas. On wide (and even inclined) plains the “Absolute” in TornadoN method is the most suitable. “Relative” should preferably be used for slightly uneven terrain with a higher base station location. The angle of incidence near the mobile should not be too small. “Slope height” is suited to locations in a geographic basin or on a central mountain midst a plain, even if the incidence is glancing
Siemens AG Information and Communication Mobile Networks
Model Tuning Concepts TornadoN (macro)
Released: ICM N MS P6 Review: Runge / Gomolka Date:
Author:
J.Gomolka / D. Boera
20.02.2013
Page 20 of 20 Page(s)
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