in door dominant path model

An Introduction to the Indoor Dominant Path

Prediction Model

Responsible Editor:

René Wahl AWE Communications GmbH

Otto-Lilienthal-Str. 36 D-71034 Böblingen

Phone: +49 70 31 71 49 7 - 21 Fax: +49 70 31 71 49 7 - 12

[email protected]

Issue Date Changes

V1.0 Sept. 2005 First version of document

V2.0 Nov. 2006 Update due to a new release of the IDP (Version 2.0)

Indoor Dominant Path Prediction Model 1

© by AWE Communications GmbH November 2006

1 Motivation Ray-optical propagation models are still very time-consuming – even with accelerations like preprocessing. And what is even more important, they rely on a very accurate vector database. Small errors in the database influence the accuracy of the prediction. On the other hand, empirical models rely on dedicated propagation effects (for example the direct ray (COST 231 Multi Wall). A comparison of different prediction models is presented in figure 1-1.

Figure 1-1: Predictions with COST 231 Multi Wall (left), Ray Tracing (center) and Indoor Dominant Path Model (right). Analyzing typical propagation scenarios shows that in most cases one propagation path contributes more than 90% (in linear scale) of the total energy (see figure 1-2). The DPM (Dominant Path Model) determines exactly this dominant path between the transmitter and each receiver pixel. So the computation time compared to ray tracing is reduced significantly and the accuracy is nearly identical to ray tracing [3].

Figure 1-2: Typical Channel Impulse Response

T

R

T

R

T

R

Figure 1-3: Comparison of different propagation methods (empirical model, ray-optical model and Dominant Path Model)

Empirical models (like COST 231 model) consider only the direct path between a transmitter and a receiver pixel (left part of figure 1-3). Ray Tracing models (like IRT, figure 1-3 middle) determine numerous paths. As shown in figure 1-3 on the right, DPM determines only the most relevant path, which leads to short computation times.



As a consequence of the above mentioned properties and restrictions of the available prediction models, a new Propagation Model has been developed, the Dominant Path Prediction Model (DPM). The main characteristics of this model: � The dependency on the accuracy of the vector database is reduced (compared to ray

tracing). � Only the most important propagation path is considered, because this path delivers the

main part of the energy. � No time-consuming preprocessing is required (in contrast to IRT). � Short computation times. � Accuracy reaches or exceeds the accuracy of ray-optical models.

The Dominant Path Model offers different sub models for several applications. The sub models are: � IDP: Indoor Dominant Path Model for indoor scenarios � UDP: Urban Dominant Path Model for urban scenarios � RDP: Rural Dominant Path Model for rural scenarios

In this document the Indoor Dominant Path Model (IDP) is described. Documents about UDP and RDP are available on the website of AWE Communications (http://www.awe-communications.com). Figures 1-4 and 1-5 present some predictions with the sub models UDP and RDP(only for information).

Figure 1-4: Predictions with Urban Dominant Path Model (UDP): Area of Hong Kong (left) and Manhattan (right).

Figure 1-5: Predictions with Rural Dominant Path Model (RDP): Mountain Matterhorn (Switzerland) with some propagation paths (left) and a part of the Grand Canyon (USA) (right).



2 Indoor Dominant Path Prediction Model The IDP determines the dominant path between transmitter and each receiver pixel [4]. For the computation of the field strength (as well as power or path loss) several propagation effects are considered. The equation used for the computation of the fieldstrength E in dBµV/m:

0 0 0

dBµV 1104.77 10 log ( , )m m

n m c

j k t ti j k

dE p f i t w g pc

ϕ= = =

= − ⋅ ⋅ − − + + +

∑ ∑ ∑

Equation for the field strength E in dBµV/m. The definition of the parameters :

d Distance (along the path) between transmitter and current receiver pixel p Path loss exponent, depending on the current propagation situation f(φ,i) Function for individual attenuation for each interaction i of all n interactions tj Transmission (penetration) loss for each penetration j through a wall wk Gain caused by waveguiding for each pixel along one propagation path gt Directional gain of transmitting antenna in direction of propagation path pt Power of transmitter in dBm

As described above, d is the length of the path between transmitter and current receiver pixel. p is the path loss exponent. The value of p depends upon the current propagation situation. In buildings with a lot of furniture p = 2.4 is used, whereas in empty halls p = 2.0 is suggested. The function f yields the loss (in dB) which is caused by a diffraction. All diffraction losses are accumulated along one propagation path. The transmission (penetration) losses tj are also accumulated along the one propagation path, as well as the waveguiding effects. Waveguiding occurs if the wave propagates through a long corridor and reflections on the walls appear (see next section). In this case an additional waveguiding gain wk in dB is determined and accumulated along all pixels on the current propagation path. The directional gain of the antenna gt (in direction of the propagation path) and the power pt are also considered in the equation. Some examples for predictions with the IDP are shown in figure 2-1.

Figure 2-1: Several applications for the Indoor Dominant Path Model: Combined Indoor/Urban prediction (left), prediction inside the front part of a car (middle) and prediction inside a tunnel (right).



3 Features The IDP offers several features which are designed to yield very accurate prediction results. In the following sections these features are described.

3.1 Waveguiding The IDP considers diffractions and transmissions explicitly. Reflections and scattering are included empirically. To consider reflections (and scattering), an empirically determined waveguiding factor is introduced. This waveguiding factor takes into account, that a wave propagating in a long corridor, will be reflected on the walls leading to less attenuation compared to free space. Thus, waveguiding effects can be expressed as an additional gain in dB. The gain due to waveguiding for a building is shown in figure 3-1.

Figure 3-1: The gain in dB caused by waveguiding

3.2 Different Path Loss Exponents The IDP can distinguish between several propagation modes. Figure 3-2 shows the different modes and the transitions between them. The modes are: � LOS: Line-of-Sight between transmitter and

receiver. � OLOS (obstructed LOS): transmitter and

receiver are located in the same room and the path needs no penetration of a wall – but there is no line of sight between transmitter and receiver

� NLOS (non-LOS): At least one penetration through a wall is required between transmitter and receiver.

For each mode a different path loss exponent can be defined and is considered in the computation.

LOSarea

OLOSareaInteraction

Penetrationthrougha wall

NLOSarea

Penetrationthrougha wall

Figure 3-2: Several propagation modes.

Different path loss exponents are suggested, because the electromagnetic wave is distorted after multiple interactions or transmissions and thus it will be higher attenuated, which can be expressed with an increased path loss exponent. Suggested values for the different modes are:

LOS 2.0 to 2.2 OLOS 2.1 to 2.3 NLOS 2.3 to 2.5



3.3 Multiple Layers The IDP offers a 3D mode to compute predictions on different layers. This is useful if predictions are requested on several floors of a multifloor building. In figure 3-3 a multifloor building together with predictions on five layers is shown.

Figure 3-3: Prediction on different floors.

3.4 Combined Network Planning The DPM offers the CNP mode which makes it possible to combine urban and indoor predictions [5]. In the urban region the prediction is computed with the Urban Dominant Path Model (UDP), its settings and the resolution selected for the urban environment. For the prediction in the indoor area the Indoor Dominant Path Model (IDP) with a finer resolution is used. Thus, the resolution is automatically adapted to urban or indoor requirements. The settings of the Dominant Path Model(e.g. path loss exponents and interaction losses) are also adapted to the current situation. Figure 3-4: Combined scenario: urban and indoor.

3.5 Automatic Calibration In the beginning of 2007 an auto-calibration mode for the IDP will be offered. With this tool, all relevant parameters of the IDP can be determined automatically if measurements (see example in figure 3-5) are available for the scenario. This is in particular useful, if the construction of the scenario differs much from the “average”. The parameters which will be calibrated automatically are: � Path loss exponents in LOS, OLOS and NLOS areas � Interaction losses

Figure 3-5: Example of measurements for Auto-Calibration.



4 Configuration Important for accurate prediction results is the configuration of the prediction model. The configuration of IDP is described in detail in this chapter.

4.1 Path Loss Exponents Among the most relevant parameters are the path loss exponents. As explained in chapter 2 the path loss exponents describe the strength of the attenuation depending on the distance. A higher path loss exponent leads to a higher attenuation in same distance. The Indoor Dominant Path Model distinguishes between three modes, as described in detail in section 3.2.

Figure 4-1: Predictions with different path loss exponents for LOS area (left: 2.0, center: 2.3, right: 2.6). Figure 4-1 shows a comparison of predictions with different path loss values for the LOS area.

4.2 Interaction Losses Each change in the direction of propagation due to an interaction (e.g. diffraction) along a propagation path causes an additional attenuation. The maximum attenuation can be defined in the settings dialog.

α1

φ2φ1

α2

φ

f( ,i)φ

Figure 4-2: Interaction losses.

In figure 4-2 the function for the determination of the attenuation depending on the angle is depicted. As shown in the figure, the attenuation increases linearly until an angle φ1 is reached. Beyond this angle, the attenuation is constant at its maximum value α2. The maximum value α2 can be defined in the settings dialog (ϕ1 and α1 are determined automatically).



4.3 Advanced Waveguiding Effects As mentioned in section 4, the Indoor Dominant Path Model includes waveguiding effects to achieve highly accurate results. Two parameters are offered to configure the waveguiding module. The first parameter is used to define the maximum distance to walls to be included in the determination of the waveguiding factor. With the second parameter the weight of the waveguiding factor in the computation of the path loss is defined (1.0 is suggested. Values below 1.0 reduce the influence of the waveguiding factor and values above 1.0 increase it).

4.4 Determination of Propagation Paths The determination of the propagation paths works in two different modes, depending on prediction height and transmitter height. 2D Mode: The 2D mode is automatically chosen, if transmitter and prediction height are located on the same floor. Then the prediction is computed on only one layer. Figure 4-3shows a scenario ideally suited for the 2D mode of the IDP: Transmitter and prediction layer are on one floor. The prediction layer is colored in blue. The red arrow is a possible propagation path to a receiver pixel (on the prediction layer) in this scenario.

Figure 4-3: Scenario with 2D mode.

3D Mode: The IDP activates automatically the 3D mode, if transmitter and prediction height are not on one floor or several prediction heights are entered by the user. An additional layer is automatically inserted on the height of the transmitter. This is always done in 3D mode. The additional layer is colored green in figure 4-4. Fig. 4-4shows a typical multifloor scenario. The blue colored prediction layers are entered by the user in the prediction heights edit box in ProMan. Possible propagation paths are the red polylines. Another example for the 3D mode is presented in figure 4-5. Here the IDP chooses automatically the 2D mode, as both transmitter and prediction height are on the same floor. So only one layer would be used for the prediction and there would be no opportunity for a wave propagation over the obstacle, so the predicted field strength would be too pessimistic behind the black obstacle. Because of that it is recommended to insert additional prediction heights, if obstacles are available in a floor. After insertion of a prediction layer in 3.5m height, the wave can propagate over the obstacle (see red propagation path).





5 Examples To demonstrate the performance of IDP, some comparisons to measurements are presented in this chapter. Further comparisons to measurements can be found on the website of AWE Communications (http://www.awe-communications.com).

5.1 Typical modern office building Measurements were carried out in the Institute for Radio Frequency Technology of the University of Stuttgart [2]. This office block is made of steel and concrete and shown in figure 5-1.

Figure 5-1: Typical modern office block (left) and the database of the building (right). In this scenario several measurements with different transmitter locations have been accomplished. In each case the transmitter was located on a height of 0.9 m, its power was 20 dBm and the frequency was set to 1800 MHz. A prediction for a transmitter location and the difference between prediction and measurement is shown in figure 5-2.

Figure 5-2: Prediction for transmitter location 12 (left) and difference (prediction-measurement) (right).



The statistical evaluation of the differences for seven transmitters and the corresponding measurements are shown in table 5-1. In all cases the standard deviations are about 4 or 5 dB and the mean values are below 4 dB.

Transmitter Mean Value Std. Dev.

4 2.23 dB 3.92 dB

5 -1.84 dB 3.62 dB

6 -0.95 dB 4.47 dB

8 0.97 dB 4.04 dB

12 -2.66 dB 2.81 dB

Table 5-1: Statistical evaluation for modern office building.

5.2 Typical historical office building The second scenario is an old office building made of brick and wood. This building is the Institute of Communications and Radio-Frequency Engineering of the University of Vienna. The building and the database are both shown in figure 5-3.

Figure 5-3: Typical historical building (left) and the corresponding database (right). Measurements with several different transmitter locations were carried out [1]. Each time, the antenna height was 1.60 m, the power was 1 Watt and the frequency 1800 MHz. In figure 5-4 the result of the prediction for a transmitter is shown, as well as the difference to the measurements.

Figure 5-4: Prediction for transmitter location 0 (left) and difference (prediction-measurement).



The statistical evaluation of the differences for two transmitters and the corresponding measurements is shown in table 5-2.

Transmitter Mean Value Std. Dev.

0 1.60 dB 4.67 dB

3 -5.71 dB 5.94 dB

Table 5-2: Statistical evaluation for historical office building.

6 Further Information For further information you are invited to visit AWE Communications’ website

http://www.awe-communications.com or to send an e-mail at the responsible editor of this document

[email protected]

7 References [1] R. Gahleitner: Radio Wave Propagation in and into Urban Buildings, PhD thesis, University

of Vienna, 1994 [2] G. Wölfle: Adaptive Modelle zur Funknetzplanung und zur Berechnung der

Empfangsqualität in Gebäuden, PhD thesis, University of Stuttgart, 1999 [3] G. Wölfle, R. Wahl, P. Wildbolz, P. Wertz: Dominant Path Prediction Model for Indoor and

Urban Scenarios, 11th COST 273 MCM, Duisburg (Germany), Sep. 2004 [4] G. Wölfle, R. Wahl, P. Wertz, P. Wildbolz, F. Landstorfer: Dominant Path Prediction Model

for Indoor Scenarios, German Microwave Conference (GeMIC) 2005, Ulm (Germany), April 2005

[5] R. Wahl, G. Wölfle: Combined Urban and Indoor Radio Network Planning using the Dominant Path Propagation Model, 1st European Conference on Antennas and Propagation, Nice, France, Nov. 2006