An Introduction to the Urban Intelligent Ray Tracing (IRT) Prediction Model

Transcription

1 An Introduction to the Urban Intelligent Ray Tracing (IRT) Prediction Model Responsible Editor: Dr.-Ing. Reiner Hoppe AWE Communications GmbH Otto-Lilienthal-Str. 36 D Böblingen Phone: Fax: Issue Date Changes V1.0 Sept First version of document

2 Urban Intelligent Ray Tracing (IRT) Prediction Model 2 1 Motivation Radio transmission in urban environments is subject to strong multipath propagation. Dominant characteristics in these scenarios are the shadowing by buildings, the wave guiding in street canyons due to multiple reflections and the propagation around corners by diffraction. To consider these effects in a propagation model, it is necessary to gain knowledge of all relevant propagation paths. As such paths can not be predicted by the widely applied empirical models as HataOkumura their accuracy is limited. The following figure 1-1 compares such an empirical model to the sophisticated 3D Intelligent Ray Tracing model visualising significant differences in the predicted coverage for a given omni base station in an urban scenario: The Hata-Okumura model predicts only a distance dependent path loss neglecting the influence of the data. In building contrary the 3D Intelligent Ray Tracing takes into account all relevant propagation phenomena, showing the influence of the street layout as well as the signal blockage behind large buildings. Similar differences occur when comparing the corresponding network layouts (see best server plots as presented in figure 1-2). Accordingly the Hata-Okumura model clearly underestimates and the coverage interference, especially when wave guiding and over rooftop propagation effects are dominant. By using highly accurate predictions of coverage and interference the network ressources (sites, frequencies, ) can be used more efficiently. Figure 1-1: Predictions with Hata-Okumura (left) and Urban 3D Intelligent Ray Tracing (right) by AWE Communications GmbH Figure 1-2: Network layout with Hata-Okumura (left) and with Urban 3D Intelligent Ray Tracing (right). September 2005

3 Urban Intelligent Ray Tracing (IRT) Prediction Model 3 The 3D Intelligent Ray Tracing (IRT) is based on vector building data describing the underlying scenario. Every building is modeled as a vertical cylinder with polygonal ground plane and an uniform height above street level. Such databases can be converted from various different formats as e.g. MapInfo, ArcView, DXF, MSI Planet, Aircom Enterprise, Nokia Netact, Ericsson TEMS, Siemens Tornado, Additionally, the material properties (wall thickness, permittivity, conductivity) of the building surfaces can be taken into account, which is important for the calculation of the reflection and diffraction coefficients and also for the penetration into buildings. Figure 1-3 presents such a database including some ray paths between transmitter and receiver. The additional consideration of the topography is possible if the urban area is not flat (either building height relative to ground level or absolute to sea level). Tx Figure 1-3: Typical Propagation Scenario in Urban Environment (Paris) Rx Besides the above mentioned advantages the ray-optical model can also be used to predict the wideband parameters of the radio channel, as e.g. channel profile, delay spread, angular spread. Such information can be used for more detailed investigations and the design of new system architectures (e.g. MIMO). Most often the advantages of using a deterministic Ray Tracing prediction model are well known, however due to challenging computational demands such models are seldomly used in practice. As a consequence of this restriction the Intelligent Ray Tracing (IRT) model has been developed. To overcome the long prediction times the building database is preprocessed once, reducing the prediction times to minutes and so comparable to the times needed for the usage of empirical models. Subsequently the characteristics of the IRT model can be summarised as follows: Full 3D Ray Tracing based on vector building data No calibration required due to the deterministic approach (GTD/UTD) Short computation times Supreme accuracy of ray-optical approach For the installation of mobile radio systems, wave propagation models are necessary to determine the coverage and interference situation. The predictions are required for a proper coverage planning, the determination of multipath effects as well as for interference and cell calculations, in order to optimise the network configuration. The usage of the highly accurate 3D IRT prediction model will reduce the costs for network roll-out and extension as the network layout can be optimised already at the planning stage.

4 Urban Intelligent Ray Tracing (IRT) Prediction Model 4 2 Urban Intelligent Ray Tracing Prediction Model The IRT model computes for each valid path between transmitter and receiver the corresponding path loss. For the computation of the path loss (as well as received power or field strength) all dominant propagation effects are considered. The following equation is used for the computation of the path loss in db: Ltotal = LFS + GTx + LInteraction = log f MHz + 10 n log d km + GTx + LInteraction Equation for the path loss computation in db The definition of the parameters is as follows: d Distance (along the path) between transmitter and current receiver pixel GTx Directional gain of transmitting antenna in direction of propagation path LInteraction Loss in propagation path due to reflection, diffraction or penetration f Frequency of transmission Path loss exponent, depending on the propagation situation (before/after n breakpoint) The path loss is computed for each ray between transmitter and receiver according to the above equation. For the computation of each ray s contribution, not only the free space loss has to be considered but also the loss due to the interaction of the electromagnetic waves with the existing obstacles, i.e. due to reflection, diffraction and penetration. Finally, all the contributions are superposed, i.e. the individual power contributions are added as the different paths are uncorrelated. Some examples for prediction results performed with the IRT model are shown in figure 2-1: Figure 2-1: Two examples for application of the Urban IRT Model: Prediction of a large area in Hong Kong (left) and a prediction in Stuttgart (right). by AWE Communications GmbH September 2005

5 Urban Intelligent Ray Tracing (IRT) Prediction Model 5 3 Features The IRT offers several features which are designed to yield highly accurate prediction results. In the following sections these features are described. 3.1 Selection of Considered Path Classes As already mentioned there are different types of rays (direct, reflected, diffracted) especially when the combination of reflection and diffraction is considered. The path loss occurring along these rays depends on the path length but mainly on the number as well as the kind of interactions. Therefore the different ray types are arranged in classes according to the expected path loss (see Table 3-1). When doing the prediction, the type of rays that should be considered during the prediction is defined using these so called path classes. Path Class Description 1 Direct path 2 Single reflection 3 Double reflection 4 Single diffraction 5 Triple reflection 6 One reflection + one diffraction 7 Double diffraction 8 Two reflections + one diffraction Table 3-1: Classification of the different rays in path classes Inside a specific class a similar interaction loss for the different rays can be assumed and with increasing order of the path class the interaction loss to be expected increases. For the prediction a maximum number of path classes which is considered can be defined. A typical maximum value for the prediction is path class 7. The usage of higher path classes increases only the consideration of the wave guiding effect. Figure 3-1 shows the coverage achieved by the individual path classes subscribing the need for multiple reflections and especially diffractions. Class 1: direct Class 2: 1 Reflection Class 3: 2 Reflections Class 4: 1 Diffraction Class 5: 3 Reflections Class 6: 1 Reflection + 1 Diffraction Class 7: 2 Diffractions Class 8: 2 Reflections + 1 Diffraction Figure 3-1: Coverage achieved for the individual path classes (only contribution of individual class is indicated).

6 Urban Intelligent Ray Tracing (IRT) Prediction Model Breakpoint and Different Path Loss Exponents According to free space propagation the receivced power drops 20 db per decade of distance, i.e. propagation exponent is 2.0. However this law is no longer valid from a certain distance on as the direct and ground-reflected ray tend to interact destructively. Based on this two-path model (see Figure 3-2) the breakpoint distance can be derived. After the breakpoint distance a propagation exponent of 4.0 has to be used. In order to further refine the propagation prediction there is also a distinction between LOS and NLOS scenarios, i.e. altogether four propagation exponents are used: Path Loss [db] ,1 0,3 Two path model Free space model 1,0 3,16 10,0 Distance [km] Figure 3-2: Path loss for 900 MHz and Tx height of 30m, Rx height 1.5m ( berakpoint distance 1.7 km) LOS: Line-of-sight between transmitter and receiver. NLOS (no LOS): Transmitter and receiver are not located in the same street and the path needs at least one interaction LOS beyond breakpoint: Line-of-sight between transmitter and receiver and the distance of the path is larger than the distance of the breakpoint. NLOS beyond breakpoint: Same as NLOS but the distance between transmitter and receiver is larger than the breakpoint distance. For each state, a different path loss exponent can be defined and is considered in the computation. The distance of the breakpoint depends on the frequency, the height of the transmitter and on the height of the receiver. The consideration of the breakpoint is reasonable, because in distances beyond the breakpoint, the attenuation of the waves increases, as destructive interference between different propagation paths occurs. Suggested values for the different modes are: LOS before breakpoint 2.0 NLOS before breakpoint 2.6 LOS after breakpoint 4.0 NLOS after breakpoint Empirical Diffraction and Reflection Model Besides the distance dependent path loss the additional attenuation by reflection and diffraction has to be taken into account in the wave propagation model. The diffraction process in ray theory is the propagation phenomena which explains the transition from the lit region to the shadow regions behind the corner or over the rooftops. Diffraction by a single wedge can be solved in various ways: empirical formulas, perfectly absorbing wedge, Geometrical Theory of Diffraction (GTD) or Uniform Theory of Diffraction (UTD). Generally, the wedge diffraction coefficient is inversely proportional to the square-root of the frequency, i.e. the coefficient decreases with increasing frequency. Therefore the effect of diffraction can be neglected for millimetre waves (f > 30 GHz). The IRT considers both an empirical diffraction

7 Urban Intelligent Ray Tracing (IRT) Prediction Model 7 coefficient (varying with the incidence and diffracted angles and the material properties of the considered wedge) and the deterministic approach according to UTD. Also for the reflections either the physical model according to the Fresnel coefficients or the empirical reflection coefficient is taken into account. In general (see Figure 3-1) the reflections are present in LOS regions (leading to wave guiding) and are rather limited in NLOS areas. Therefore the diffractions are dominant in the shadowed areas. For the diffraction the angles as presented in Figure 3-3 have to be considered. Diffraction Cone Angle Relations ϕ φ φ (2-n) Valid for n = Figure 3-3: Diffraction cone and occuring angles π t t Figure 3-4: Empirical diffraction model based on a two step approach The empirical diffraction model computes the total diffraction loss in a two step approach based on the three parameters L incident,min, L incident,max and L diffracted. In the first step the loss depending on the angle of incidence is determined (see Figure 3-4 left). For this purpose the first two parameters L incident,min and L incident,max are evaluated. The corresponding loss increases with decreasing angle of incidence (i.e. increasing grazing incidence). Based on this first result the second curve (shown in the Figure 3-4 right) is evaluated, leading to the total diffraction loss. Basically the diffraction loss increases with increasing interaction angle. However for a difference of 180 the total diffraction loss is fixed to 6 db as in this special case the incident wave propagates straight forward while half of the space is shadowed according to the given obstacle. Therefore the range of possible total diffraction losses is given by [6 db; L incident,max + L diffracted ]. The given angle dependence is derived from the uniform diffraction theory (UTD) by the evaluation of measurements with different materials (brick, concrete) in an anechoic chamber and can be varied with the parameters L incident,min, L incident,max and L diffracted within appropriate limits. With these three parameters the model can be calibrated with measurements. For the strong physical UTD model also three parameters are used: dielectricity (epsilon), permeability (µ) and conductivity (sigma). The Figure 3-5 compares both models and shows that similar results can be achieved. It is recommended to use the empirical diffraction model as this model can be much better tuned (due to the usage of db values).

8 Urban Intelligent Ray Tracing (IRT) Prediction Model 8 Figure 3-5: Comparison between empirical diffraction model on the left with parameters (40, 15, 10 db) and UTD model on the right with parameters (4, 0.01, 1) For the reflections either the physical model according to the Fresnel coefficients or the empirical reflection coefficient is taken into account. Figure 3-6 shows the Fresnel reflection coefficient depending on the incident angle. The presented dependency is modelled linearly within the empirical model. This means the defined reflection loss specifies the loss for grazing incidence (90 ) while for perpendicular incidence only half of the loss is considered. 3.4 Vegetation Figure 3-6: Reflection coefficient depending on angle of incidence If data concerning vegetation in the city (parks, etc.) is available, the IRT model considers this data in the prediction. Vegetation databases describe areas of vegetation with polygonal cylinders (individual height for each cylinder possible). Additional attenuations can be defined for each vegetation element. So all paths penetrating the vegetation get an additional loss (in db/m) and all pixels inside the vegetation object can also get an additional loss (in db). This makes sure that the prediction results are not too optimistic, if vegetation is present. Figure 3-7 shows a map with such vegetation objects. Figure 3-7: Building database including vegetation zones

9 Urban Intelligent Ray Tracing (IRT) Prediction Model Topography The terrain profile should be considered for the prediction if the considered urban area is not flat. For significant standard deviations of the terrain heights databases in pixel format are required with resolutions about m. The IRT model considers the topographical data, if available, and the buildings are shifted accordingly. This is especially important in case of hilly terrain environment, as otherwise the prediction results would be too optimistic in the shadowed areas. Figure 3-8 shows an example of a building database including topography. Figure 3-8: Example of building database including topography 3.6 Combined Network Planning The IRT model offers the CNP mode which makes it possible to combine urban and indoor propagation environments. In the urban region the prediction is computed with the Urban Intelligent Ray Tracing (U-IRT), in the resolution selected for the urban domain. For the prediction in the indoor area the Indoor Intelligent Ray Tracing (I-IRT) with a finer resolution is utilised. Thus, the resolution is automatically adapted to the urban and indoor requirements. Figure 3-9 shows the principle of the CNP approach combining urban and indoor environments (i.e. including detailed consideration of indoor scenario with windows and indoor walls). Figure 3-9: Combined database: urban and indoor 4 Configuration In order to ensure accurate prediction results the configuration of the IRT prediction model has to be adapted to the considered frequency range. Basically the default values as presented in chapter 4.1 can be used. For very large urban areas it can be switched from the 3D-IRT to the 2x2D-IRT model, reducing the computation effort for the preprocessing and prediction. This approach is presented in chapter Settings The most relevant parameters for the IRT prediction model are the path loss exponents and the defined diffraction and reflection losses. For the considered path classes it is recommended to use the default value. As explained in chapter 3.2 the path loss exponents model the attenuation depending on the distance. A higher path loss exponent leads to a higher attenuation for the same distance. The Urban IRT model distinguishes between four exponents (LOS/NOS and before/after the

10 Urban Intelligent Ray Tracing (IRT) Prediction Model 10 breakpoint), as described in detail in section 3.2. Additionally the interaction losses due to diffraction and reflection have to be taken into account which are frequency dependent. Therefore it is proposed to use the default values as indicated in Table 4-1. Default parameters f = 900 MHz f = 1800 MHz f = 3500 MHz Reflection loss 9 db 9 db 9 db Diffraction loss min. (incident ray) 5 db 8 db 11 db Diffraction loss max. (incident ray) 12 db 15 db 18 db Diffraction loss (diffracted ray) 5 db 5 db 5 db Exponent before BP 2.0 LOS 2.6 NLOS 2.0 LOS 2.6 NLOS 2.0 LOS 2.6 NLOS Exponent after BP 4.0 LOS 4.0 NLOS 4.0 LOS 4.0 NLOS 4.0 LOS 4.0 NLOS Table 4-1: Default settings for different frequency ranges 4.2 2x2D Approach Out of numerous transmitter to receiver propagation paths, the most dominant ones have to be selected to obtain the total received power with moderate computation time. A useful acceleration to the process of ray path finding under the consideration of the main propagation mechanisms is the limitation to two orthogonal planes (2x2D). Rooftop diffracted paths are included in the vertical plane approach, while around building diffracted paths are modeled within the transverse plane approach. The propagation in both the vertical and the transverse plane is twodimensionally regarded. However, the determination of the building corners in the Figure 4-1: 2x2D approach for vertical and transverse plane is not necessarily performed in a transverse propagation plane. horizontal plane. This principle can be also considered for the Intelligent Ray Tracing approach which leads to the following 2 x 2D models: 2x2D (2D-H IRT + 2D-V IRT) The preprocessing and as a follow on also the determination of propagation paths is done in two perpendicular planes. One horizontal plane (for the wave guiding, including the vertical wedges) and one vertical plane (for the over rooftop propagation including the horizontal edges). In both planes the propagation paths are determined similar to the 3D- IRT by using ray optical methods. This approach neglects the contributions by reflections at the building walls which are in most cases only relevant for the LOS areas. 2x2D (2D-H IRT + COST231-W-I) This model treats the propagation in the horizontal plane in exactly the same way as the previously described model, i.e. by using ray optical methods (for the wave guiding, including the vertical wedges). The over rooftop propagation (vertical plane) is taken into account by evaluating the COST 231-Walfisch-Ikegami model. By using this model only the propagation in the horizontal plane is determined by ray-optical methods taking into account the vertical wedges of the buildings, while the over rooftop propagation is modelled by an empirical approach.

11 Urban Intelligent Ray Tracing (IRT) Prediction Model 11 5 Examples To demonstrate the performance of the IRT, some comparisons to measurements are presented in the GSM, UMTS and WiMAX bands. Further comparisons to measurements can be found in [3]. 5.1 GSM900 Band The mobile network operator Vodafone D2 (formerly Mannesmann Mobilfunk) provided a measurement campaign including three different routes and a database for the city of Munich. These data has been used to perform a benchmark of urban propagation models in the framework of the COST 231 project [1]. The Figure 5-1 shows the database of Munich (buildings incl. topography). The antenna height was 13 m, transmitter power 10 W and the frequency was 947 MHz (GSM900). In Figure 5-2 the prediction result is shown as well as the difference to one of the three measurements routes. Figure 5-1: Database of Munich with topography Figure 5-2: Prediction result with 3D IRT (left) and difference (prediction-measurement) for route 2 The statistical evaluation of the differences for each measurement route is shown in Table 5-1. Route Mean Value Std. Dev Table 5-1: Statistical evaluation for Munich

12 Urban Intelligent Ray Tracing (IRT) Prediction Model UMTS Band Measurements in Monte Carlo (Monaco) were carried out by the Fraunhofer Gesellschaft in the framework of the EU funded project MAESTRO [2]. The database of the relevant part of Monaco is shown in Figure 5-3. Due to the steep terrain the consideration of the topography is very important in this scenario. Figure 5-3: Database of Monaco, buildings with topography, 3D view (left) and 2D (right) The transmitter (blue marked in the Figure 5-3) is located above the harbour area where the measurements have been carried out. The antenna height is 17 m above ground, the transmitting power was set to 31 dbm and the transmitting frequency was 2.2 GHz. A prediction result is shown in Figure 5-4, as well as the difference to one of the three measurement routes. Figure 5-4: Prediction result with the 3D IRT model (left) and difference (prediction-measurement) for measurement route 50 The statistical evaluation of the differences for each of the measurement routes is shown in Table 5-2. Route Mean Value Std. Dev Table 5-2: Statistical evaluation for Monaco

13 Urban Intelligent Ray Tracing (IRT) Prediction Model WiMAX Band Together with Alcatel, measurements in the WiMax frequency range of 3.5 GHz were made in the city of Stuttgart (Germany). The database is shown in Figure 5-5. Figure 5-5: Database of Stuttgart: 3D view (left) and 2D (right) with topography Measurements for three transmitter locations were carried out. All of the transmitter powers were set to 34 dbm, the frequency was 3.5 GHz. The antenna heights varied between 31 m and 51 m. The prediction result for transmitter 1 is presented in Figure 5-6, as well as the difference to the corresponding measurement route. Figure 5-6: Prediction result with the 3D IRT model for transmitter location 1 (left) and difference (prediction-measurement) for corresponding measurement The statistical evaluations of the differences for three transmitters and the corresponding measurements are shown in Table 5-3. Further comparisons to measurement campaigns are e.g. given in [3]. Transmitter Mean Value Std. Dev Table 5-3: Statistical evaluation for Stuttgart by AWE Communications GmbH September 2005

14 Urban Intelligent Ray Tracing (IRT) Prediction Model 14 6 Further Information For further information you are invited to visit AWE Communications website or to send an to the responsible editor of this document Reiner.Hoppe@awe-communications.com 7 References [1] E. Damosso (Ed.): Final report of COST 231: Digital Mobile Radio towards future Generation Systems, European Comission, Bruxelles, [2] T. Heyn, A. Heuberger, C. Keip: Propagation Measurements for the Characterization of a Hybrid Mobile Channel in S-Band, 14 th IST Mobile & Wireless Communications Summit Dresden (Germany), June [3] T. Rautiainen, G. Wölfle, and R. Hoppe: Verifying Path Loss and Delay Spread Predictions of a 3D Ray Tracing Propagation Model in Urban Environments, 56th IEEE Vehicular Technology Conference (VTC) Fall, Vancouver (British Columbia, Canada), Sept