Propagation Models & Scenarios:Rural /Suburban© 2012 by AWE Communications GmbH                           www.awe-com.com
Contents       • Overview: Propagation Scenarios        - Rural and Suburban: Pixel Databases (Topography and Clutter)    ...
Propagation Scenarios Propagation Scenarios (1/2)   Different types of cells in a cellular network       • Macrocells     ...
Propagation Scenarios Propagation Scenarios (2/2)                                Macrocell                Microcell       ...
Wave Propagation Models Propagation Models       • Different types of environments require different propagation models   ...
Topography and Clutter Data Databases: Topographical Databases Topographical database (DEM, Digital Elevation Model)      ...
Topography and Clutter Data Databases: Clutter (morpho, land usage) Databases Clutter database (land usage)               ...
Topography and Clutter Data Databases: Clutter (morpho, land usage) Databases Clutter database (land usage) Example: Germa...
Topography and Clutter Data Databases: Clutter (morpho, land usage) Databases   Properties defined for each clutter class ...
Topography and Clutter Data Databases: Clutter (morpho, land usage) Databases   Properties defined for each clutter class ...
Topography and Clutter Data Databases: Clutter (morpho, land usage) Databases   Properties defined for each frequency band...
Topography and Clutter Data Propagation Models       • Hata-Okumura           • 4 submodels (open/suburban/medium urban/de...
Topography and Clutter Data Propagation Models: Hata-Okumura       • Four submodels           • open                 • med...
Topography and Clutter Data Propagation Models: Two-Ray & Knife-Edge Diffraction       • Computation of direct and ground ...
Topography and Clutter Data Propagation Models: Two-Ray & Knife-Edge Diffraction       • Superposition of clutter heights ...
Topography and Clutter Data Propagation Models: Two-Ray & Knife-Edge Diffraction       • Variation of obstacles even in sa...
Topography and Clutter Data Propagation Models: Two-Ray & Knife-Edge Diffraction       • Clearance impacts the propagation...
Topography and Clutter Data Propagation Models: Two-Ray & Knife-Edge Diffraction Examples       Prediction in Baden-Württe...
Topography and Clutter Data Propagation Models: ITU P.1546       • For terrestrial radio circuits over land paths, sea pat...
Topography and Clutter Data Propagation Models: Dominant Path Model Determination of Paths        Analysis of types of we...
Topography and Clutter Data Propagation Models: Dominant Path Model Computation of field strength/path loss        Path l...
÷ å                              ø                   t       t       =       -    ⋅       ç ÷-          j   +       +     ...
Topography and Clutter Data Propagation Models: Dominant Path Model Examples                                            18...
Topography and Clutter Data Propagation Models: Dominant Path Model Examples       Prediction of an area in Switzerland (6...
Topography and Clutter Data Propagation Models: Dominant Path Model Examples                                              ...
Topography and Clutter Data Propagation Models: Ray Tracing Usage of Digital Surface Models       • includes buildings, ve...
Topography and Clutter Data Propagation Models: Ray Tracing                                     • Multipath propagation co...
Topography and Clutter Data Propagation Models: Ray Tracing Determination of Paths                                • The Ra...
Topography and Clutter Data Propagation Models: Ray Tracing Examples2012             © by AWE Communications GmbH   28
Topography and Clutter Data Propagation Models: Ray Tracing Results       Channel Impulse Response                        ...
Topography and Clutter Data Propagation Models: Ray Tracing Results       Spatial Chanel Impulse Response (3D)2012        ...
Rural Evaluation Evaluation with Measurements       I.     Area around Grab/Murrhardt, Germany       II.    Area around Lu...
Rural Evaluation        Scenario I: Area around Grab/Murrhardt, Germany           Scenario Information  Topo. difference  ...
Rural Evaluation       Scenario I: Area around Grab/Murrhardt, Germany        Prediction with Hata-Okumura Model       Pre...
Rural Evaluation       Scenario I: Area around Grab/Murrhardt, Germany           Difference of prediction with Hata-     D...
Rural Evaluation       Scenario I: Area around Grab/Murrhardt, Germany                                                 Sta...
Rural Evaluation       Scenario II: Area around Ludwigsburg, Germany        Scenario Information Topo. difference         ...
Rural Evaluation       Scenario II: Area around Ludwigsburg, Germany        Prediction with Hata-Okumura Model       Predi...
Rural Evaluation       Scenario II: Area around Ludwigsburg, Germany         Difference of prediction with Hata-     Diffe...
Rural Evaluation       Scenario II: Area around Ludwigsburg, Germany                                               Statist...
Rural Evaluation       Scenario III: Hjorring, Denmark                                                                 Sce...
Rural Evaluation       Scenario III: Hjorring, Denmark       Prediction with Hata-Okumura Model with       Prediction with...
Rural Evaluation       Scenario III: Hjorring, Denmark          Difference of prediction with Hata-         Difference of ...
Rural Evaluation       Scenario III: Hjorring, Denmark                                               Statistical Results  ...
Rural Evaluation       Scenario IV: Jerslev, Denmark                                                                 Scena...
Rural Evaluation       Scenario IV: Jerslev, Denmark        Prediction with Hata-Okumura Model with      Prediction with R...
Rural Evaluation       Scenario IV: Jerslev, Denmark           Difference of prediction with Hata-       Difference of pre...
Rural Evaluation       Scenario IV: Jerslev, Denmark                                               Statistical Results    ...
Rural Evaluation       Scenario V: Ravnstrup, Denmark                                                                 Scen...
Rural Evaluation       Scenario V: Ravnstrup, Denmark        Prediction with Hata-Okumura Model with        Prediction wit...
Rural Evaluation       Scenario V: Ravnstrup, Denmark           Difference of prediction with Hata-             Difference...
Rural Evaluation       Scenario V: Ravnstrup, Denmark                                                Statistical Results  ...
Summary  Features of WinProp Rural Module       • Highly accurate propagation models for various scenarios             Emp...
Further InformationFurther information: www.awe-com.com2012             © by AWE Communications GmbH   53
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Propagation rural

  1. 1. Propagation Models & Scenarios:Rural /Suburban© 2012 by AWE Communications GmbH www.awe-com.com
  2. 2. Contents • Overview: Propagation Scenarios - Rural and Suburban: Pixel Databases (Topography and Clutter) - Urban: Vector databases (Buildings) and pixel databases (Topography) - Indoor: Vector databases (Walls, Buildings) • Wave Propagation Model Principles - Multipath propagation - Reflection - Diffraction - Scattering - Antenna pattern • Topography and Clutter Data - Map data - Propagation models - Evaluation with measurements2012 © by AWE Communications GmbH 2
  3. 3. Propagation Scenarios Propagation Scenarios (1/2) Different types of cells in a cellular network • Macrocells • Cell radius > 2 km • Coverage • Microcells • Cell radius < 2 km • Capacity (hot spots) • Picocells • Cell radius < 500 m • Capacity (hot spots)2012 © by AWE Communications GmbH 3
  4. 4. Propagation Scenarios Propagation Scenarios (2/2) Macrocell Microcell Picocell Vector data Database type Raster data Vector data Raster data Topography 2.5D building (vector) 3D building Database Clutter Topography (pixel) 3D indoor objects Hata-Okumura Knife Edge Diffraction Motley Keenan Path Loss Two Ray COST 231 WI COST 231 MW Prediction Models Knife Edge Diffraction Ray Tracing Ray Tracing Dominant Path Dominant Path Dominant Path r < 30 km r < 2000 m Radius r < 200 m r > 2 km r > 200 m2012 © by AWE Communications GmbH 4
  5. 5. Wave Propagation Models Propagation Models • Different types of environments require different propagation models • Different databases for each propagation model • Projects based on clutter/topographical data or vector/topographical data • Empirical and deterministic propagation models available • CNP used to combine different propagation environments Types of databases • Pixel databases (raster data) • Topography, DEM (Digital Elevation Model) • Clutter (land usage) • Vector databases • Urban Building databases (2.5D databases  polygonal cylinders) • Urban 3D databases (arbitrary roofs) • Indoor 3D databases2012 © by AWE Communications GmbH 5
  6. 6. Topography and Clutter Data Databases: Topographical Databases Topographical database (DEM, Digital Elevation Model) • Arbitrary resolution Recommended: 20 - 30 m • Elevation in meter (converters available for feet,…) • Interpolation of undefined pixels possible • Geodetic or UTM coordinates • More than 200 coordinate datum supported • Display of additional vector data layers (e.g. streets, districts,….) Example: Detroit, USA • Index or single database files (incl. multiple resolutions) • Curvature of earth surface considered (optionally)2012 © by AWE Communications GmbH 6
  7. 7. Topography and Clutter Data Databases: Clutter (morpho, land usage) Databases Clutter database (land usage) • Individual class assigned to each pixel • Class ID with individual properties • Frequency dependent attenuation • Clutter heights • Clutter clearance • Electrical properties of ground • Selection of prediction submodels (Hata-Submodels) • Class either defined by local receiver coordinates or Example: Detroit, USA weighted along the path from transmitter to receiver2012 © by AWE Communications GmbH 7
  8. 8. Topography and Clutter Data Databases: Clutter (morpho, land usage) Databases Clutter database (land usage) Example: Germany • 12 classes • 50m resolution2012 © by AWE Communications GmbH 8
  9. 9. Topography and Clutter Data Databases: Clutter (morpho, land usage) Databases Properties defined for each clutter class • Name (and ID) • Weight (if dominant class along path between Tx and Rx is determined) • Color (on display) • Height of objects in class (for LOS and diffraction loss) • Clearance of objects in class (for LOS and diffraction loss) • Selection of Hata submodel • Dense Urban • Medium Urban • Suburban • Open Area • Definition of individual electrical properties (losses, ground properties) for multiple frequency bands2012 © by AWE Communications GmbH 9
  10. 10. Topography and Clutter Data Databases: Clutter (morpho, land usage) Databases Properties defined for each clutter class • Fixed clutter heights and • Statistically distributed clutter clearance radii heights and clearance radii2012 © by AWE Communications GmbH 10
  11. 11. Topography and Clutter Data Databases: Clutter (morpho, land usage) Databases Properties defined for each frequency band • Frequency band margins • Additional loss (in dB) for all models except Hata-Okumura • Additional loss (in dB) for Hata-Okumura depending on Hata sub model: • Dense Urban • Medium Urban • Suburban • Open Area • Electrical properties of ground for selected models (to determine reflection loss): • Deterministic Two Ray • 3D Scattering Frequency band properties2012 © by AWE Communications GmbH 11
  12. 12. Topography and Clutter Data Propagation Models • Hata-Okumura • 4 submodels (open/suburban/medium urban/dense urban) • Akeyama Extension • COST 207 for frequencies in the 2 GHz band Hata-Okumura • Two Ray Model • Direct ray and ground reflected ray • Either deterministic (with check of visibility and check of reflection) or empirical (assuming always LOS) • Knife Edge Diffraction • Consideration of topography in vertical plane between Tx and Rx (additionally to Hata or Two Ray Model) Knife Edge Diffraction • ITU P.1546 • Interpolation from empirical field strength curves • Dominant Path Model • Full 3D path searching algorithm • 2D/3D Ray Tracing Model • Ray tracing algorithm in 3D or in vertical plane Dominant Path2012 © by AWE Communications GmbH 12
  13. 13. Topography and Clutter Data Propagation Models: Hata-Okumura • Four submodels • open • medium urban • suburban • dense urban • Two different sub-model modes • homogenous - same sub-model for whole area • individual – model selection depending on clutter class at mobile station • Akeyama Extension (close to Tx) • COST 207 Extension (frequencies in 2 GHz band) • Topography between Tx and Rx not considered (e.g. shadowing due to hills, etc.) • Frequency band between 150 and 2000 MHz2012 © by AWE Communications GmbH 13
  14. 14. Topography and Clutter Data Propagation Models: Two-Ray & Knife-Edge Diffraction • Computation of direct and ground reflected ray • Additional diffraction loss in shadowed areas (frequency dependent) • Topography between Tx and Rx considered (e.g. shadowing due to hills, etc.) • Possible evaluation of Fresnel zone2012 © by AWE Communications GmbH 14
  15. 15. Topography and Clutter Data Propagation Models: Two-Ray & Knife-Edge Diffraction • Superposition of clutter heights to terrain profile • Propagation model considers topography and clutter heights Topo Profile individual height for each clutter class Clutter Profile Forest Open Buildings Skyscr. Buildings Street Forest Open Forest Buildings Superposition2012 © by AWE Communications GmbH 15
  16. 16. Topography and Clutter Data Propagation Models: Two-Ray & Knife-Edge Diffraction • Variation of obstacles even in same clutter class • Heights of each class can be statistically distributed (individual parameters) Topo Profile Individual statistical distribution of height for each clutter class Clutter Profile Forest Open Buildings Skyscr. Buildings Street Forest Open Forest Buildings Superposition2012 © by AWE Communications GmbH 16
  17. 17. Topography and Clutter Data Propagation Models: Two-Ray & Knife-Edge Diffraction • Clearance impacts the propagation (for each class defined individually) BS MS 1 MS 2 Without Clearance BS MS 1 MS 2 With Clearance Clearance Buildings: 2 Grid 2 Grid 0.5 Grid Clearance Forest: 0.5 Grid2012 © by AWE Communications GmbH 17
  18. 18. Topography and Clutter Data Propagation Models: Two-Ray & Knife-Edge Diffraction Examples Prediction in Baden-Württemberg (10000 km²) with Two-Ray plus Knife-Edge Diffraction model pt0=57 dBm, f=2200 MHz, ht=67 m, omni antenna2012 © by AWE Communications GmbH 18
  19. 19. Topography and Clutter Data Propagation Models: ITU P.1546 • For terrestrial radio circuits over land paths, sea paths and/or mixed land-sea paths • interpolation/extrapolation from empirically derived field strength curves as functions of distance, antenna height, frequency and percentage of time • includes corrections of the results obtained from interpolation/extrapolation to account for terrain clearance and terminal clutter obstructions2012 © by AWE Communications GmbH 19
  20. 20. Topography and Clutter Data Propagation Models: Dominant Path Model Determination of Paths  Analysis of types of wedges in scenario  Generation of tree with convex wedges  Searching best path  Computation of path loss T 6 1 Layer 1 2 4 5 Layer 2 4 5 2 R 5 2 4 5 T 2 3 4 Layer 3 R 5 4 5 2 4 R 2 R Layer 4 R R concave wedges convex wedges 1 3 6 2 4 52012 © by AWE Communications GmbH 20
  21. 21. Topography and Clutter Data Propagation Models: Dominant Path Model Computation of field strength/path loss  Path length d  Path loss exponents before and after breakpoint p  individual interaction losses f(φ,i) for each interaction i of all n interactions  Gain due to waveguiding wk at c pixels along the path  Gain gt of base station antenna  Power pt of transmitter n dBμV æd e 104.77 10 p log f ( , i) g p2012 © by AWE Communications GmbH 21
  22. 22. ÷ å ø t t = - ⋅ ç ÷- j + + ⋅ m m i=02012 © by AWE Communications GmbH 22
  23. 23. Topography and Clutter Data Propagation Models: Dominant Path Model Examples 181 km Prediction of the Grand Canyon (16900 km², 2.6 Megapixel) with Rural Dominant Path Model pt0=40 dBm, f=948 MHz, ht=25 m, omni antenna2012 © by AWE Communications GmbH 22
  24. 24. Topography and Clutter Data Propagation Models: Dominant Path Model Examples Prediction of an area in Switzerland (63 km², 632000 Pixel) with Rural Dominant Path Model pt0=10 Watt, f=948 MHz, ht=25 m, omni antenna2012 © by AWE Communications GmbH 23
  25. 25. Topography and Clutter Data Propagation Models: Dominant Path Model Examples Prediction of a high mountain (‘Matterhorn’) in Switzerland with Rural Dominant Path Model pt0=10 Watt, f=948 MHz, ht=25 m, omni antenna2012 © by AWE Communications GmbH 24
  26. 26. Topography and Clutter Data Propagation Models: Ray Tracing Usage of Digital Surface Models • includes buildings, vegetation, and roads, as well as natural terrain features • Conversion of topography from pixel to vector format • Consideration of land usage in vector format Additional obstacles in vector format2012 © by AWE Communications GmbH 25
  27. 27. Topography and Clutter Data Propagation Models: Ray Tracing • Multipath propagation considered • Dominant effects: diffraction, reflection and shadowing • Ray with multiple reflections and diffractions are determined (incl. different combinations) • Angle tolerance for reflections to emulate scattering • Electrical properties of ground can be defined for each clutter class individually • Either full 3D or 2D in vertical plane • Uncorrelated or correlated superposition of contributions (rays) • Optional post-processing with Knife Edge Diffraction model possible2012 © by AWE Communications GmbH 26
  28. 28. Topography and Clutter Data Propagation Models: Ray Tracing Determination of Paths • The Ray Tracing computes all rays for each receiver point individually and guarantees the consideration of each ray as well as a constant resolution. • For the computation of the rays, not only the free space loss has to be considered but also the loss due to the reflections and (multiple) diffraction. This is either done using a physical deterministic model or using an empirical model.2012 © by AWE Communications GmbH 27
  29. 29. Topography and Clutter Data Propagation Models: Ray Tracing Examples2012 © by AWE Communications GmbH 28
  30. 30. Topography and Clutter Data Propagation Models: Ray Tracing Results Channel Impulse Response Angular Profile2012 © by AWE Communications GmbH 29
  31. 31. Topography and Clutter Data Propagation Models: Ray Tracing Results Spatial Chanel Impulse Response (3D)2012 © by AWE Communications GmbH 30
  32. 32. Rural Evaluation Evaluation with Measurements I. Area around Grab/Murrhardt, Germany II. Area around Ludwigsburg, Germany III. Hjorring, Denmark IV. Jerslev, Denmark V. Ravnstrup, Denmark2012 © by AWE Communications GmbH 31
  33. 33. Rural Evaluation Scenario I: Area around Grab/Murrhardt, Germany Scenario Information Topo. difference 394 m Resolution 50.0 m 91.0 m, 43.8 dBm, Transmitter 1259.05 MHz Prediction height 1.5 m 3D view of the database (z-axis scaled with factor 5)2012 © by AWE Communications GmbH 32
  34. 34. Rural Evaluation Scenario I: Area around Grab/Murrhardt, Germany Prediction with Hata-Okumura Model Prediction with Rural Dominant Path with Knife-Edge-Diffraction Extension Model2012 © by AWE Communications GmbH 33
  35. 35. Rural Evaluation Scenario I: Area around Grab/Murrhardt, Germany Difference of prediction with Hata- Difference of prediction with Rural Okumura Model with Knife-Edge- Dominant Path Model and Diffraction Extension and measurement measurement (cut-out) (cut-out)2012 © by AWE Communications GmbH 34
  36. 36. Rural Evaluation Scenario I: Area around Grab/Murrhardt, Germany Statistical Results Hata-Okumura Model with Knife-Edge-Diffraction Rural Dominant Path Scenario Extension Mean Comp. Mean Comp. Std. Dev. Std. Dev. Value Time Value Time [dB] [dB] [dB] [s] [dB] [s] Grab/Murrhardt 18.01 9.26 3 5.79 8.95 62 Remark: A standard PC with an AMD Athlon64 2800+ processor and 1024 MB of RAM was used to determine the computation times of the predictions.2012 © by AWE Communications GmbH 35
  37. 37. Rural Evaluation Scenario II: Area around Ludwigsburg, Germany Scenario Information Topo. difference 205 m 3D view of the database (z- Resolution 50.0 m axis scaled with factor 5) 41.0 m, 49.0 dBm, Transmitter 438.92 MHz Prediction height 1.5 m2012 © by AWE Communications GmbH 36
  38. 38. Rural Evaluation Scenario II: Area around Ludwigsburg, Germany Prediction with Hata-Okumura Model Prediction with Rural Dominant Path with Knife-Edge-Diffraction Extension Model2012 © by AWE Communications GmbH 37
  39. 39. Rural Evaluation Scenario II: Area around Ludwigsburg, Germany Difference of prediction with Hata- Difference of prediction with Rural Okumura Model with Knife-Edge- Dominant Path Model and Diffraction Extension and measurement (cut-out) measurement (cut-out)2012 © by AWE Communications GmbH 38
  40. 40. Rural Evaluation Scenario II: Area around Ludwigsburg, Germany Statistical Results Hata-Okumura Model with Knife-Edge-Diffraction Rural Dominant Path Scenario Extension Mean Comp. Mean Comp. Std. Dev. Std. Dev. Value Time Value Time [dB] [dB] [dB] [s] [dB] [s] Ludwigsburg -1.54 8.31 3 -9.76 7.39 9 Remark: A standard PC with an AMD Athlon64 2800+ processor and 1024 MB of RAM was used to determine the computation times of the predictions.2012 © by AWE Communications GmbH 39
  41. 41. Rural Evaluation Scenario III: Hjorring, Denmark Scenario Information Topo. difference 28.0 m Resolution 50.0 m 12.0 m, 40 dBm, Transmitter 970 MHz Prediction height 3.0 m Terrainprofile of database 3D view of terrainprofile of database (z-axis stretched with facor 10)2012 © by AWE Communications GmbH 40
  42. 42. Rural Evaluation Scenario III: Hjorring, Denmark Prediction with Hata-Okumura Model with Prediction with Rural Dominant Path Model Knife-Edge-Diffraction Extension2012 © by AWE Communications GmbH 41
  43. 43. Rural Evaluation Scenario III: Hjorring, Denmark Difference of prediction with Hata- Difference of prediction with Rural Dominant Okumura Model with Knife-Edge- Path Model and measurement Diffraction Extension and measurement2012 © by AWE Communications GmbH 42
  44. 44. Rural Evaluation Scenario III: Hjorring, Denmark Statistical Results Hata-Okumura Model with Knife-Edge-Diffraction Rural Dominant Path Scenario Extension Mean Comp. Mean Comp. Std. Dev. Std. Dev. Value Time Value Time [dB] [dB] [dB] [s] [dB] [s] Hjorring 0.13 10.04 <1 2.05 7.85 <1 Remark: A standard PC with an AMD Athlon64 2800+ processor and 1024 MB of RAM was used to determine the computation times of the predictions.2012 © by AWE Communications GmbH 43
  45. 45. Rural Evaluation Scenario IV: Jerslev, Denmark Scenario Information Topo. difference 20.9 m Resolution 50.0 m 12.0 m, 40 dBm, Transmitter 970 MHz Prediction height 3.0 m Terrainprofile of database 3D view of terrain profile of database (z-axis stretched with factor 10)2012 © by AWE Communications GmbH 44
  46. 46. Rural Evaluation Scenario IV: Jerslev, Denmark Prediction with Hata-Okumura Model with Prediction with Rural Dominant Path Model Knife-Edge-Diffraction Extension2012 © by AWE Communications GmbH 45
  47. 47. Rural Evaluation Scenario IV: Jerslev, Denmark Difference of prediction with Hata- Difference of prediction with Rural Dominant Okumura Model with Knife-Edge- Path Model and measurement Diffraction Extension and measurement2012 © by AWE Communications GmbH 46
  48. 48. Rural Evaluation Scenario IV: Jerslev, Denmark Statistical Results Hata-Okumura Model with Knife-Edge-Diffraction Rural Dominant Path Scenario Extension Mean Comp. Mean Comp. Std. Dev. Std. Dev. Value Time Value Time [dB] [dB] [dB] [s] [dB] [s] Jerslev -5.36 6.42 <1 -0.68 6.77 <1 Remark: A standard PC with an AMD Athlon64 2800+ processor and 1024 MB of RAM was used to determine the computation times of the predictions.2012 © by AWE Communications GmbH 47
  49. 49. Rural Evaluation Scenario V: Ravnstrup, Denmark Scenario Information Topo. difference 45.4 m Resolution 50.0 m 12.0 m, 40 dBm, Transmitter 970 MHz Prediction height 3.0 m Terrainprofile of database 3D view of terrain profile of database (z-axis stretched with factor 10)2012 © by AWE Communications GmbH 48
  50. 50. Rural Evaluation Scenario V: Ravnstrup, Denmark Prediction with Hata-Okumura Model with Prediction with Rural Dominant Path Model Knife-Edge-Diffraction Extension2012 © by AWE Communications GmbH 49
  51. 51. Rural Evaluation Scenario V: Ravnstrup, Denmark Difference of prediction with Hata- Difference of prediction with Rural Okumura Model with Knife-Edge- Dominant Path Model and measurement Diffraction Extension and measurement2012 © by AWE Communications GmbH 50
  52. 52. Rural Evaluation Scenario V: Ravnstrup, Denmark Statistical Results Hata-Okumura Model with Knife-Edge-Diffraction Rural Dominant Path Scenario Extension Mean Comp. Mean Comp. Std. Dev. Std. Dev. Value Time Value Time [dB] [dB] [dB] [s] [dB] [s] Ravnstrup 0.48 6.60 <1 1.86 6.56 <1 Remark: A standard PC with an AMD Athlon64 2800+ processor and 1024 MB of RAM was used to determine the computation times of the predictions.2012 © by AWE Communications GmbH 51
  53. 53. Summary Features of WinProp Rural Module • Highly accurate propagation models for various scenarios Empirical: Hata-Okumura, ITU P.1546, … Semi-Empirical: Two-Ray plus Knife-Edge diffraction, … Deterministic (ray optical): 3D Dominant Path, 3D Ray Tracing, 2x2D Ray Tracing Optionally calibration of models with measurements possible – but not required as the models are pre-calibrated • Topography and Clutter or Vector Data Obstacles described by clutter or vector data Consideration of material properties (also vegetation objects can be defined) Consideration of topography (pixel databases) • Antenna patterns Either 2x2D patterns or 3D patterns • Outputs Signal level (path loss, power, field strength) Channel impulse response, angular profile (direction of arrival)2012 © by AWE Communications GmbH 52
  54. 54. Further InformationFurther information: www.awe-com.com2012 © by AWE Communications GmbH 53

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