Propagation urban

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Propagation urban

  1. 1. Propagation Models & Scenarios:Urban© 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 Vector Data (buildings and/or vegetation) - 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 Vector Data Databases: Vector Building Databases • 3D vector oriented database • Buildings as vertical cylinders with polygonal ground-planes • Uniform height above street-level Example: New York • Limitation to vertical walls and flat roofs • Individual material properties of building surfaces • Topography can be considered optionally2012 © by AWE Communications GmbH 6
  7. 7. Topography and Vector Data Consideration of Topography for Vector Scenarios Topographical databases: • Topography in pixel databases • Resolutions of 20-30 m Consideration in Prediction: • Shift transmitter and receiver • Shift buildings due to the topo • Approximation of topo with triangles Effects on results: • Additional shadowing by hills • Changing LOS-area of the transmitter • No additional rays (scattering at topo)2012 © by AWE Communications GmbH 7
  8. 8. Topography and Vector Data Databases: Vector Building Databases Special features Courtyards and Towers Vegetation areas Vegetation areas are polygonal cylinders. Rays get an additional attenuation (dB/m) when passing the cylinder and receiver pixels inside cylinder get an additional loss Multiple Courtyards and Towers2012 © by AWE Communications GmbH 8
  9. 9. Topography and Vector Data Databases: Material Properties Global catalogue for different construction materials (at various frequency bands) (In WallMan via menu Edit  Materials  Import)  User can add or modify materials2012 © by AWE Communications GmbH 9
  10. 10. Topography and Vector Data Databases: Material Properties Local material database (in building database) • only relevant for objects in this database • independent of global material catalogue (modification of global catalogue does not affect material properties of objects in database) • can be updated with materials from global material catalogue Settings of local material database • individual material properties for different frequency bands (always the properties of the frequency band closest to TX frequency is used) • Material (incl. all properties) is assigned to objects (walls/buildings) • Always all material properties must be defined even if they are not required for the selected propagation model • Individual colors can be assigned to the materials for better visualization2012 © by AWE Communications GmbH 10
  11. 11. Topography and Vector Data Databases: Material Properties Properties of a material • Properties affecting all propagation models Transmission Loss (in dB) • Properties affecting Ray Tracing & Dominant Path Model Reflection Loss (in dB) • Properties affecting Ray Tracing • GTD/UTD related properties • Relative Dielectricity • Relative Permeability • Conductance (in S/m) • Empirical reflection/diffraction model • Reflection Loss (in dB) • Diffraction Loss Incident Min (in dB) • Diffraction Loss Incident Max (in dB) • Diffraction Loss Diffracted (in dB)2012 © by AWE Communications GmbH 11
  12. 12. Topography and Vector Data Propagation Models • COST 231 Walfisch-Ikegami • Homogenous parameters (street width, building height,…) for whole area • Individual determination of parameters according to buildings in vertical plane between Tx and Rx • Ray Tracing • 3D Ray Tracing IRT (with preprocessing) • 2x2D Ray Tracing IRT (horiz. and vertical plane) • 3D Ray Tracing SRT (standard, no preprocessing) • Dominant Path Model • 3D path searching2012 © by AWE Communications GmbH 12
  13. 13. Topography and Vector Data Propagation Models: COST 231 Walfisch-Ikegami • Model accepted by ITU-R • Evaluating building profile between transmitter and receiver (vertical plane) • Consideration of additional losses due to building data • Reasonable results for Tx above rooftops For Tx below rooftops limited accuracy (no wave guiding) • No multipath propagation considered Transmitter Considered propagation path Receiver Buildings considered for determination of parameters2012 © by AWE Communications GmbH 13
  14. 14. Topography and Vector Data Propagation Models: COST 231 Walfisch-Ikegami WinProp: Vertical plane is analyzed for each predicted pixel individually! Parameters of the model obtained from the buildings in the vertical plane ht hr h Roof w b d • Height of transmitter hTX • Mean value of building heights hroof • Height of receiver hRX • Mean value of widths of roads w • Mean value of building separation b Vertical profile with topography2012 © by AWE Communications GmbH 14
  15. 15. Topography and Vector Data Propagation Models: COST 231 Walfisch-Ikegami Parameters of the model gained from the buildings in the vertical plane d f LOS: lb  42,6 dB  26  lg  20  lg km MHz l0  lrts  l msd l rts  lmsd  0 NLOS: lb  l0 lrts  lmsd  0 f r Free space loss l0 : l0  32,44 dB  20  lg  20  lg MHz km w f h  r h Rooftop loss lrts : lrts  16,9 dB  10  lg  10  lg  20  lg Roof m MHz m d f b Over rooftop loss lmsd : lmsd  lbsh  k a  k d  lg  k f  lg  9  lg km MHz m  ht  Roof  h  18  lg1   ht hRoof with lbsh   m  0 ht  hRoof Factors k a and k d Valid for: f MHz ................... 800 - 2000 Empir. Correction of antenna heights ht m ................................. 4 - 50 Faktor k f hr m ................................. 1 - 3 Adaption to different building densities d m ........................... 20 - 50002012 © by AWE Communications GmbH 15
  16. 16. Topography and Vector Data Propagation Models: Ray Tracing • Multipath propagation • Dominant effects: diffraction and reflection • Up to 6 reflections and 2 diffractions are determined as well as combinations • Computation of the path loss with Fresnel coefficients (for reflection) and GTD/UTD model (for diffraction). Alternative: Scalable empirical reflection/diffraction model for prediction of path loss along the ray • Uncorrelated superposition of contributions (rays) • Either full 3D or 2x2D (horizontal and vertical plane) • Post-processing with Knife Edge Diffraction model possible2012 © by AWE Communications GmbH 16
  17. 17. Topography and Vector Data Propagation Models: Ray Tracing Types of rays to be determined • Different types of rays: direct, reflected, diffracted, scattered • Definition of max. number for each interaction type • Definition of total interaction number • Selection of Fresnel & GTD/UTD or empirical interaction model • Additional thresholds for computation of paths2012 © by AWE Communications GmbH 17
  18. 18. Topography and Vector Data Propagation Models: Ray Tracing Direct Single Reflection Double Single Reflection Diffraction2012 © by AWE Communications GmbH 18
  19. 19. Topography and Vector Data Propagation Models: Ray Tracing Triple Single Reflection Reflection + Single Diffraction Double Double Diffraction Reflection + Single Diffraction2012 © by AWE Communications GmbH 19
  20. 20. Topography and Vector Data Propagation Models: Intelligent Ray Tracing (IRT) Considerations to accelerate the time consuming process of path finding: • Deterministic modelling generates a large number of rays, but only few of them deliver most of the power • Visibility relations between walls and edges are independent of transmitter location • Adjacent receiver pixels are reached by rays with only slightly different paths  Single pre-processing of the building database with determination of the visibility relations between buildings reduces computation time2012 © by AWE Communications GmbH 20
  21. 21. Topography and Vector Data Propagation Models: Intelligent Ray Tracing (IRT) Pre-processing of the Building Database • Subdivision of the walls into tiles • Subdivision of the vertical and horizontal edges into segments  min • Subdivision of the prediction area into receiving points (grid)  max min • stored information for each visibility relation: max • angle between the elements • distance between centres • example: visibility between a tile and a receiver pixel Tile Prediction Pixel • projection of connecting straight lines Segment Center of Tile into xy-plane and perpendicular plane Center of horiz. Segm. Center of vert. Segm. • 4 angles for each visibility relation2012 © by AWE Communications GmbH 21
  22. 22. Topography and Vector Data Propagation Models: Intelligent Ray Tracing (IRT) Prediction with Pre-processed Data • Determination of all tiles, segments and receiving points, which are visible from the transmitter PREDICTION • Computation of the angles of incidence belonging to Direct ray these visibility relations 1.interaction PREPRO- • Recursively processing of CESSING all visible elements incl. consideration of the 2.interaction angular conditions • Tree structure is very fast and efficient 3.interaction transmitter receiving point tile / segment2012 © by AWE Communications GmbH 22
  23. 23. Topography and Vector Data Problem of Database Accuracy in Ray Tracing models T T Ray Tracing Building error2012 © by AWE Communications GmbH 23
  24. 24. Topography and Vector Data Propagation Models: Urban Dominant Path (UDP) Typical Channel Impulse Response  Dominant Path (single path)  Determination of path with full 3D One path approach dominates  Unlimited number of interactions (changes of orientation)  Parameters of path determined (e.g length, number of interactions, angles,….) and used to compute path loss with semi-deterministic equations Full 3D approach  Optional consideration of wave guiding possible (wave guiding factor, based on reflection loss of walls)  Short prediction time  High accuracy2012 © by AWE Communications GmbH 24
  25. 25. Topography and Vector 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 25
  26. 26. Topography and Vector Data Propagation Models: Dominant Path Model Computation of Path Loss  Path length l  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 Ω  Gain gt of base station antenna n æ 4p ö L 20 log 10 p log (l ) f ( , i) g l÷ ø å t2012 © by AWE Communications GmbH 26
  27. 27. = ç ÷+ + j +W+ i=02012 © by AWE Communications GmbH 27
  28. 28. Topography and Vector Data Propagation Models: Dominant Path Model Parameters for prediction (1/2)2012 © by AWE Communications GmbH 27
  29. 29. Topography and Vector Data Propagation Models: Dominant Path Model Parameters for prediction (2/2)  Acceleration for large areas  Adaptive Resolution Management  Path loss exponents before and after breakpoint can be defined individually TX  Breakpoint distance/computation can be adapted to the users needs  Definition of different path loss exponents for LOS (Line of Sight) and OLOS (Obstructed Line of Sight)  Interaction losses (at points where the Wave guiding factor path changes its orientation) can be defined  Individual reflection loss assigned to buildings influences wave guiding effect2012 © by AWE Communications GmbH 28
  30. 30. Topography and Vector Data Propagation Models: Preprocessing with WallMan Single pre-processing of building database required only for IRT model Project File Pre-processed Pre-processing Pre-processing Database Files (*.pre) (Computation) (oib, ocb opb) Database Extensions: Original Binary Database file *.odb Outdoor Data Binary (*.odb) *.ocb Outdoor COST Binary Materials (electrical properties) can still be modified after pre-processing. *.oib Outdoor IRT Binary Re-assignment of materials to objects *.opb Outdoor Dom. Path Binary is not possible after pre-processing.2012 © by AWE Communications GmbH 29
  31. 31. Topography and Vector Data Propagation Models: Comparison COST 231 Walfisch-Ikegami Ray Tracing (3D IRT) Dominant Path (3D) Computation time: < 1 min Computation time: 3 min Computation time: < 1 min Preprocessing time: < 1 min Preprocessing time: 30 min Preprocessing time: < 1 min Not very accurate High accuracy in region of Tx High accuracy everywhere Limited accuracy far away2012 © by AWE Communications GmbH 30
  32. 32. Topography and Vector Data Propagation Models: Indoor Penetration Constant Level Model Exponential Decrease Model Variable Decrease Model Considers defined Considers defined Considers defined transmission loss transmission loss transmission loss Homogeneous indoor level Additional exponential Additional exponential decrease towards the decrease towards the interior Subtracting defined interior with attenuation rate with definable attenuation transmission loss from depending on building rate (default 0.6 dB/m) average level at outer walls depth (~ 0.1 dB/m)2012 © by AWE Communications GmbH 31
  33. 33. Topography and Vector Data Propagation Models: Prediction of LOS States  LOS: Line of sight between Tx and Rx  OLOS: Obstructed line of sight between Tx and Rx (only indoor)  NLOS: No line of sight between Tx and Rx  LOS-V: Line of sight regarding the buildings, but shadowing due to vegetation  NLOS-V: NLOS due to buildings and additional shadowing by vegetation2012 © by AWE Communications GmbH 32
  34. 34. Topography and Vector Data Sample Large Urban Scenario incl. Topography Prediction of Hong Kong (334 km², 1.5 megapixel, 22030 buildings, comp. time: 15 min) (transmit power: 40 dBm, GSM 900, directional antenna at 40 m height)2012 © by AWE Communications GmbH 33
  35. 35. Topography and Vector Data Sample Urban Scenario 2D view Prediction of Manhattan (9 km x 18 km, 15758 buildings, comp. time: 6 min)2012 © by AWE Communications GmbH 35
  36. 36. Urban Evaluation Evaluation with Measurement Data Wave Propagation Models considering  Topography and Clutter Data  Topography and Vector Data2012 © by AWE Communications GmbH 36
  37. 37. Urban Evaluation Evaluation with Measurements Investigated Scenarios: I. Helsinki, Finland II. Hong Kong, China III. Monaco, Monte Carlo IV. Munich, Germany V. Ilmenau, Germany VI. Amsterdam, Netherlands2012 © by AWE Communications GmbH 37
  38. 38. Urban Evaluation Scenario I: Helsinki, Finland Scenario Information Number of buildings 1651 Topo. difference none (flat terrain) Resolution 5m Site 1 4.0 m, 2.5 Watt, 900 MHz 3D view of database Transmitter Site 2 41.5 m, 10 Watt, 2.1 GHz Prediction heights 1.6 m, 2.5 m2012 © by AWE Communications GmbH 38
  39. 39. Urban Evaluation Scenario I: Helsinki, Finland Predictions for transmitter location 2 Prediction with COST 231 Prediction with 3D Ray Prediction with Urban Walfisch-Ikegami Tracing Dominant Path2012 © by AWE Communications GmbH 39
  40. 40. Urban Evaluation Scenario I: Helsinki, Finland Differences for transmitter location 2 Difference of prediction Difference of prediction Difference of prediction with COST 231 Walfisch- with 3D Ray Tracing and with Urban Dominant Ikegami and measurements Path and measurements measurements2012 © by AWE Communications GmbH 40
  41. 41. Urban Evaluation Scenario I: Helsinki, Finland Statistical evaluations for all transmitters Statistical Results Empirical Model Deterministic Model (e.g. COST 231 Walfisch- Site (e.g. 3D Ray Tracing or Urban Dominant Path) Ikegami) Mean Std. Comp. Mean Value Std. Dev. Comp. Time Value Dev. Time [dB] [dB] [s] [dB] [dB] [s] 2 -9.38 9.40 2 -1.04…1.94 5.92…6.30 20…32 3 -5.84 8.35 2 -3.60…4.31 5.53…7.81 18.. 32 Avg -7.61 8.88 2 -0.83...1.64 5.73...7.06 19.. 32 A standard PC with an AMD Athlon64 2800+ processor and 1024 MB of RAM was used to determine the computation times2012 © by AWE Communications GmbH 41
  42. 42. Urban Evaluation Scenario II: Hong Kong, China Scenario Information Number of buildings 3306 Topo. difference 482 m Resolution 10 m Site 1 33.0 m, 28.5 dBm, 948 MHz Transmitter 3D view of database with topography Site 2 94.0 m, 24.9 dBm, 948 MHz Prediction height 1.5 m2012 © by AWE Communications GmbH 42
  43. 43. Urban Evaluation Scenario II: Hong Kong, China Predictions for transmitter location 1 Prediction with COST 231 Walfisch-Ikegami Prediction with Urban Dominant Path Prediction with 3D Ray Tracing2012 © by AWE Communications GmbH 43
  44. 44. Urban Evaluation Scenario II: Hong Kong, China Differences for transmitter location 1 Difference of prediction with COST 231 Walfisch-Ikegami and measurements Difference of prediction with Urban Dominant Path and measurements Difference of prediction with 3D Ray Tracing and measurements2012 © by AWE Communications GmbH 44
  45. 45. Urban Evaluation Scenario II: Hong Kong, China Statistical evaluations for all transmitters Statistical Results Empirical Model Deterministic Model (e.g. COST 231 Walfisch- Site (e.g. 3D Ray Tracing or Urban Dominant Path) Ikegami) Mean Std. Comp. Comp. Mean Value Std. Dev. Value Dev. Time Time [dB] [dB] [dB] [dB] [s] [s] 1 -12.81 20.13 5 0.72…4.91 6.08 …7.56 10…127 2 1.34 9.02 5 -2.30…5.63 7.74… 7.79 16…80 Avg -5.74 14.58 5 -0.79...5.27 6.94 ...7.65 13...104 A standard PC with an AMD Athlon64 2800+ processor and 1024 MB of RAM was used to determine the computation times2012 © by AWE Communications GmbH 45
  46. 46. Urban Evaluation Scenario III: Monaco, Monte Carlo Scenario Information Number of buildings 1511 Topo. difference 646 m 3D view of database Resolution 10 m Transmitter 17.0 m, 31.0 dBm, 2.2 GHz Prediction height 1.5 m2012 © by AWE Communications GmbH 46
  47. 47. Urban Evaluation Scenario III: Monaco, Monte Carlo Predictions for transmitter location 1 Prediction with COST 231 Prediction with 3D Ray Prediction with Urban Walfisch-Ikegami Tracing Dominant Path2012 © by AWE Communications GmbH 47
  48. 48. Urban Evaluation Scenario III: Monaco, Monte Carlo Differences for measurement route 50 Difference of prediction Difference of prediction Difference of prediction with COST 231 Walfisch- with 3D Ray Tracing and with Urban Dominant Ikegami and measurements Path and measurements measurements2012 © by AWE Communications GmbH 48
  49. 49. Urban Evaluation Scenario III: Monaco, Monte Carlo Statistical evaluations for all measurements routes Statistical Results Empirical Model Deterministic Model Route (e.g. COST 231 Walfisch-Ikegami) (e.g. 3D Ray Tracing or Urban Dominant Path) Mean Value Std. Dev. Comp. Time Mean Value Std. Dev. Comp. Time [dB] [dB] [s] [dB] [dB] [s] 50 -18.71 5.74 -4.73…-2.94 3.92…4.36 52 -20.12 8.09 3 -1.94…0.08 4.97…6.17 15…141 -0.60…-0.23 58 -25.28 9.04 4.09…4.87 Avg -21.37 7.62 3 -2.30...-1.15 4.73 15...141 A standard PC with an AMD Athlon64 2800+ processor and 1024 MB of RAM was used to determine the computation times2012 © by AWE Communications GmbH 49
  50. 50. Urban Evaluation Scenario IV: Munich, Germany Scenario Information Number of buildings 2032 Topo. difference 14 m Resolution 10 m Transmitter 13.0 m, 10.0 Watt, 947 MHz 3D view of database with topography Prediction height 1.5 m2012 © by AWE Communications GmbH 50
  51. 51. Urban Evaluation Scenario IV: Munich, Germany Predictions for transmitter location 1 Prediction with COST 231 Prediction with 3D Ray Prediction with Urban Walfisch-Ikegami Tracing Dominant Path2012 © by AWE Communications GmbH 51
  52. 52. Urban Evaluation Scenario IV: Munich, Germany Differences for measurement route 0 Difference of prediction Difference of prediction Difference of prediction with COST 231 Walfisch- with 3D Ray Tracing and with Urban Dominant Ikegami and measurements Path and measurements measurements2012 © by AWE Communications GmbH 52
  53. 53. Urban Evaluation Scenario IV: Munich, Germany Statistical evaluation for all measurement routes Statistical Results Deterministic Model Empirical Model (e.g. 3D Ray Tracing or Urban Dominant Route (e.g. COST 231 Walfisch-Ikegami) Path) Mean Value Comp. Time Mean Value Comp. Time Std. Dev. [dB] Std. Dev. [dB] [dB] [s] [dB] [s] 0 -10.98 6.38 -5.26…2.80 7.13…7.17 1 -13.80 7.07 -2.01…1.34 6.20…6.73 5 14...20 2 -14.70 7.43 -3.15…0.31 7.94…8.04 Avg -13.16 6.96 5 -3.47...1.48 7.09...7.31 14...20 A standard PC with an AMD Athlon64 2800+ processor and 1024 MB of RAM was used to determine the computation times2012 © by AWE Communications GmbH 53
  54. 54. Urban Evaluation Scenario V: Ilmenau, Germany  Trajectory in Urban Marco Cell (COST reference scenario)  Tx height: 26.5 m  Tx frequency: 2.53 GHz  Tx power: 46 dBm  Receiver: high resolution 3D channel sounder (RUSK, Medav GmbH)  Receiver moving with constant speed along trajectory (~ 54/123 m)  Rx height: 1.9 m2012 © by AWE Communications GmbH 54
  55. 55. Urban Evaluation Rx Power: (Route 41a-42) [dBm] Mean Std. Dev. Measured -62.38 2.24 Simulated -62.47 2.06 Difference 0.09 0.70 Delay Spread: (Route 41a-42) [ns] Mean Std. Dev. Measured 195.33 17.11 Simulated 208.79 37.46 Difference 13.46 33.32 MIMO Capacity (2x2): (Route 41a-42) [bit/s/Hz] Mean Std. Dev. Measured 6.31 0.13 Simulated 6.48 0.21 Difference 0.17 0.202012 © by AWE Communications GmbH 55
  56. 56. Urban Evaluation Rx Power: (Route 10b-9b) [dBm] Mean Std. Dev. Measured -50.83 6.18 Simulated -50.85 5.33 Difference 0.02 1.65 Delay Spread: (Route 10b-9b) [ns] Mean Std. Dev. Measured 173.36 75.54 Simulated 172.43 70.61 Difference 0.92 27.21 MIMO Capacity (2x2): (Route 10b-9b) [bit/s/Hz] Mean Std. Dev. Measured 6.14 0.19 Simulated 6.26 0.26 Difference 0.12 0.242012 © by AWE Communications GmbH 56
  57. 57. Urban Evaluation Scenario VI: Amsterdam, Netherlands  Trajectory in Urban Marco Cell  Tx height: 29 m  Tx frequency: 2.25 GHz  Tx power: 43 dBm  Receiver: high resolution 3D-Channel Sounder (TU Eindhoven)  Receiver moving with constant speed along trajectory (~ 420 m)  Rx height: 3.5 m Bridge / Tunnel (not considered in simulation)2012 © by AWE Communications GmbH 57
  58. 58. Urban Evaluation Rx Power: [dBm] Mean Std. Dev. Measured -53.91 8.04 Simulated -53.90 7.10 Difference 0.01 4.03 Delay Spread: [ns] Mean Std. Dev. Measured 222.36 106.91 Simulated 216.07 130.23 Difference -6.29 109.63 Angular Spread (Rx): [°] Mean Std. Dev. Measured 52.05 21.15 Simulated 49.79 32.96Bridge / Tunnel Difference -2.25 24.99(not considered in simulation)2012 © by AWE Communications GmbH 58
  59. 59. Summary Features of WinProp Urban Module • Highly accurate propagation models Empirical: COST 231 Walfisch-Ikegami Deterministic (ray optical): 3D Dominant Path, 3D Ray Tracing, 2x2D Ray Tracing Optionally calibration of 3D Dominant Path Model with measurements possible – but not required as the model is pre-calibrated • Building data Models are based on 2.5D vector data of buildings 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) Delays (delay window, delay spread,…) Channel impulse response Angular profile (direction of arrival)2012 © by AWE Communications GmbH 59
  60. 60. Further InformationFurther information: www.awe-com.com2012 © by AWE Communications GmbH 60

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