bit Winprop plugin installers only replace.me#!nR0ABSBa! AWE’ file required for activation of product within ‘Atoll ‘. AWE’. This license allows the operation of both the WinProp-IRT and The papers are available for download in the public relations. MIMO Antenna Systems in WinProp AWE Communications GmbH Otto-Lilienthal-Str. 36 D Böblingen Issue Date Changes V Nov First version of document V Feb.
 
 

 

Awe winprop download

 

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Awe winprop download. WinProp propagation modeling and network planning tool

 
 

Author projectenhance View 1. Tags: propagation multiple licences reaction time 10 small cells selected modules small cells 60 small cells simulation. Background 2 2 WinProp Tool available in the market since currently version Customer has to pay for each scenario. Depending on scenario. Annual fee. Depending on selected modules. Discount for multiple licences. WallMan: Selection of group of objects and deleting objects If a group of selected objects was deleted with the new version including the acceleration of the display see April, 29 th , , some objects were still in the database but not on the display.

May, 17, WallMan WallMan: Selection of objects Selection of objects with the mouse worked not always correct object could not be selected by clicking with left mouse button on object. This was due to the acceleration of the display some days before. Now a list appears and the user selects the transmitter he wants to edit from the list. The user can additionally click on the transmitter with the mouse left button and comes directly to the transmitter properties page.

May, 15, WallMan WallMan: Urban: Simplification of building databases Urban building databases can be simplified by combing adjacent buildings with a height difference below a definable threshold. This reduces the number of buildings significantly and accelerates prediction and preprocessing time. This feature is available in WallMan Edit menu as well as during the conversion process conversion of external data formats.

May, 14, WallMan WallMan: Urban: Sequence of corners during conversion of building data The sequence of corners is now automatically correcteded during conversion and must not be done later in WallMan. Especially important if LOS-plots or coverage areas with max. Very important to analyze the LOS area and the area which can be reached with an allowed max. May, 08, ProMan ProMan: Rural: 2-Ray model: Consideration of antenna patterns Determination of antenna gain did not consider topographical height of transmitter and receiver correctly.

Topographical height for both Tx and Rx is used to determine the antenna gain. Especially important if earth curvature is considered. May, 06, ProMan ProMan: Rural: Handling of databases with undefined areas If a part of the topographical database is not available pixels set to not defined the prediction is computed for all other pixels where the undefined pixels are not needed.

ProMan: Rural: Consideration of earth curvature For all rural models it is possible to consider the curvature of the earth surface. May, 03, ProMan ProMan: Additional Layers in display Especially for rural networks: Additional data streets, borders,… can be displayed to make navigation easier in the prediction results.

These data layers can be imported from open ASCII file formats and are stored together with the topographical database and the projects — so they are always visible in all prediction results. This is now fixed! April, 29, WallMan WallMan: Acceleration of display Acceleration of display by new algorithms to draw the database on the screen.

Significantly improvement especially for large databases with many objects. April, 24, WallMan WallMan: Load Database after conversion The database is no longer automatically loaded after the conversion with a database converter. This is especially suitable for larger databases to save some time. For the definition of the preprocessing parameters it is not required to see all buildings.

So the user can define a max. Max number is set to Then each 10 th building in the list of buildings is drawn Building 0, Building 10, Building 20,….

For the computation of the preprocessing all buildings are obviously considered! This feature is only to accelerate the display re-draw for very large databases….. April, 18, ProMan ProMan: Distance Tool Distance is now always displayed in meters even if geographic coordinate systems are displayed.

ProMan: More accuracy floating point digits for geographic data When using geographic databases the accuracy of the info view and all dialogs with coordinates is now increased.

April, 16, ProMan ProMan: Display of results: Plot with distribution of values The text for the x-axis of the distribution plot was always related to the default text for the prediction result and not to the definitions in the settings.

Now the text from the settings is used. Conversion of geographic coordinates to UTM no longer required for the predictions. April, 12, DataMan DataMan: Topographical databases: Conversion of height lines If some pixels in the database are not defined the missing pixels are interpolated double bi-linear interpolation. So the conversion of height lines leads to accurate topographical pixel databases even if the separation between the lines is several 10 m.

The prediction result is displayed in two colors: Green if below threshold and red if above threshold. This helps to analyze the results if they must be above or below a given threshold. ProMan: Display of results: Symbol for directional antennas Now it is possible to define the symbol for directional antennas.

Instead of the arrow also a cross similar to omnidirectional antenna is possible especially important for linear antennas diploes and monopoles which have no directional radiation in the horizontal plane. April, 10, V5. Now the transition is only computed if both areas are not identical. This is fixed. So the user had to reset the window and restart the zoom if the selected area was not the one he was interested in.

Additionally it is possible to move the selected part of the data matrix with the cursor keys or with the mouse. So time-consuming zoom-in-and-zoom-out procedures are no longer required.

April, 02, ProMan Rural: Project Files If the unit of the transmitter was set to dBm, the value in the dialog was wrong after loading the project again Value in dBm was converted to W, but unit was still dBm. The prediction used the correct value. GUI: Reading of prediction results with no data If prediction results with no data 0 lines and 0 columns were read, ProMan crashed.

ProMan reports the problem in a message box and does not crash. Rural: Modification of the prediction area The upper right corner of the empirical and deterministic prediction area and the upper right corner of the empirical area were modified to correspond to the lower left corner of the empirical prediction area plus an integer multiple of the resolution.

Network Planning: Error messages if prediction files are not available If the selected prediction files are not available, an error message appears. Still possible to compile the GUIs in a single executable for easier handling of the software.

This was harmonized leading to the same structures and code elements for indoor and urban code. March, 15, ProMan ProMan: New dialog for transmitter properties in projects The dialog for the transmitter properties in the projects was extended. It shows additionally the number of the transmitter and the transmitter can be enabled or disabled for the prediction. If the project is computed only enabled active transmitters will be computed.

All other transmitters will not be computed to save computation time. Only the computation of the transmitter located in the buildings is skipped and the computations continues with the next transmitter. ProMan: Urban IRT Progress bar: Output of Tx number The number of the current transmitter and the total number of transmitters is additionally displayed in the progress bar to show the status of the prediction.

This is especially important for projects with a high number of transmitters. This was harmonized. Now all result files are read with the same code. Old files can still be used but not in WWW evaluation version. There only new files are possible. DataMan interpreted the information always as corner. Error was limited to display of channel impulse response only.

Prediction of path loss with COST model was correct.. March, 05, WallMan WallMan: Enter new objects If new objects overlap existing objects and if the immediate check of new objects is enabled, the objects were not drawn because they produced errors.

This is correct. But no message was generated and so the user was not always aware about the reason. Now a message is generated to inform the user about the overlap of the new objects. WallMan: Paste objects at original coordinates After Cut or Copy operation the objects in the clipboard can be pasted with the mouse at their new location with the command Paste. But for some applications e. This is difficult to manage with the mouse to click to the appropriate coordinate and so the new command Paste at original coordinates is introduced.

With this command the objects are pasted at their original coordinates in the new document. February, 28, ProMan ProMan: Urban: Indoor Coverage Empirical indoor coverage could lead to memory problems in special scenarios if buildings are only partly inside the prediction area. ProMan: Urban with Empirical Indoor Coverage If empirical indoor coverage is selected, the buildings are no longer filled when loading the prediction result only for new computations.

WallMan: Paste Objects When pasting one or several objects the first corner of the object with the smallest number in the list of objects to be pasted was used as reference point. In order to ensure high accuracy the user shall define if different polarizations are used for the individual MIMO streams e. Furthermore the transmitted MIMO stream has to be selected. Generally all antennas belonging to a MIMO system must have the same carrier. Depending on the assigned Signal Group ID and the assigned MIMO stream the signals from different antennas are combined constructively or interfere each other.

In this context also the interference between different MIMO streams operating on the same carrier and Signal Group ID is considered depending on the selected option, see Figure 4. Finally the feasible modulation and coding scheme depending on the given SNIR is selected. Antenna Type Conventional antenna Antenna belonging to distributed antenna system DAS Antenna belonging to MIMO system Received Power Received power from serving cell Superposition of received power values from all antennas belonging to DAS of serving cell Superposition of received power values from all antennas transmitting the same MIMO stream as the serving cell Computation of Interference: Usually signals which are radiated on the same carrier but from different antennas interfere with each other as individual signals are transmitted.

The interfering effect can be reflected by selecting the appropriate option in the corresponding dialogue see Figure 4.

Figure 6 shows an office scenario with two antennas distributed MIMO system. Both antennas use the same carrier – otherwise there would be no co-channel interference in the scenario. Figure 7 and Figure 8 show the data rate maps for these two configurations. Because of that the signals from both antennas are superposed constructively and improve the SNIR situation. Nevertheless the max.

Figure 8 shows configuration 2 where again both antennas operate on the same carrier, but this time sites 1 and 2 form a 2×2 MIMO system.

Accordingly higher data rates can be achieved for a large part of the office building assuming here ideal separation of the different MIMO streams. Generally the performance depends also on the interference between the MIMO streams see page 4. Figure 9: Max. Whitepaper First came Planning for All rights reserved.

This document is Cisco Public Information. Page 1 of 6 Contents. Background The SmartDiagnostics wireless network is an easy to install, end-to-end machine. Technical and economical assessment of selected LTE-A schemes. GIGA seminar MIMO: What shall we do with all these degrees of freedom?

All rights reserved 1 1 Introduction 1. Heath Jr. With the explosive growth in wireless usage due in part to smart phones, tablet computers and a growing applications developer base, wireless operators are constantly looking for ways to increase the spectral. Caimi, Ph. Kerry L. Greer Jason M. Hendler January Introduction Antenna diversity has been used in wireless communication systems.

By: Peter Croy, Sr. Executive summary. Introduction 2. After the town center the transmitter-receiver distance decreased to 1. LoS conditions were dominating only interrupted by marginal non-los conditions. A Google Earth picture of Rena and the measurements is seen in Figure 4. The relative difference between hill tops and valley bottoms is larger than at Rena.

The forest is not as heavy, and the trees are lower. There is some agriculture in the area and spread housing. A Google Earth picture of Gausdal and the measurements is seen in Figure 4. Figure 4. The topographical characteristic is much the same as for Gausdal, as seen in Table 4. The valley was relatively narrow with some curvature, therefore LoS conditions were quickly lost.

The receiver van drove away from the transmitter approximately at the same elevation as the transmitter and out to a range of about The topography imposed non-los conditions for the whole path except for the start of the measurement.

The comparison between predictions and measurements are made in Section 5. The basic equipment parameters such as frequency, transmit power and antennas are the same for all the predictions. The parameters that could be selected for each model and their values are given in the text for the respective models. We have used a fixed color scaling for the path loss in all figures ranging from db to db as shown in Figure 5.

Figure 5. The Open area environment has been chosen which may not be quite representative of the actual terrain because of forest as pointed out in Section 2.

Even though Okumura-Hata is a purely empirical model, the left hand side plot shows that the WinProp implementation of it uses terrain information in some way. This was surprising to find. WinProp allows the user to add diffraction calculations to the standard Okumura-Hata model.

By adding Epstein-Peterson diffraction loss with maximum 10 knife edges the predicted coverage appears as in the right panel of the figure. The basic parameters were selected as described in Section 2.

The only other option for this model was to add a diffraction loss, and the right panel in Figure 5. We see that the predicted path loss has increased compared to the previous models, and the signal coverage is restricted to a very small area. Since terrain data were available, the model uses that information and the plot shows dependence on the terrain.

In addition to the basic parameters mentioned in Section 2. As mentioned in Section all the parameters were left at their default value, because we did not have the knowledge to alter them. As can be seen, the resulting prediction gives a very pessimistic result, compared to the other prediction models.

Again we see that the implementation of the standard Okumura-Hata surprisingly takes account of the terrain. For this path a comparison was made between Epstein-Peterson and Deygout diffraction loss.

In Figure 5. The predicted path loss is largest for the Epstein-Peterson type of diffraction. Allowing an unlimited number of knife edges did not make any significant changes to the predictions. The only observable effect was to remove the white pixels in the plots below.

For the comparison with the measurements in Section 5. For all the predictions we selected siting criteria Careful for the tranmitter and Random for the receiver reflecting the location of the transmitter high up on a hill side and a moving receiver in the bottom of the valley.

A test of the influence of the statistical parameters is made in Figure 5. This should be compared with the lower panel of Figure For the comparison with the measurements in Section 5. Terrain Irregularity m in upper plot and 90 m in lower plot Figure 5. The Confidence parameter is more sensitive than the Reliability parameter. For the comparisons in Section 5. Epstein-Peterson diffraction added in lower panel Longley-Rice area mode Two plots of the predicted path loss are shown in Figure 5.

We see that the prediction is quite dependent on this parameter. Terrain irregularity 0 m in upper plot and 90 m in lower plot Empirical two-ray Predictions using the two-ray empirical model are shown in Figure 5.

Since terrain data were available, the plot shows dependence on the terrain. This can be an AWE implementation feature. The unconventional possibility of adding diffraction losses to this model reduces coverage slightly.

The empirical Longley-Rice area mode model predicts higher path loss compared to the Okumura-Hata model, with our selection and best guess of the parameter Terrain Irregularity. The empirical Two-ray model with default parameter values gives increased path loss compared to Longley-Rice area mode, unless the Terrain Irregularity parameter in Longley-Rice is set very low. It predicts slightly lower path loss than the Longley- Rice area mode model.

The Longley-Rice point-to-point model predicts lower path loss than the Rec P. The data shown in Figure 5. In the road curve in the middle of the figure where the Longley-Rice shows blue points predicted path loss is largest the LoS path towards the transmitter is obstructed by a small hill top close to the road see picture in Figure 5.

The path loss estimate is therefore too high when the prediction program does not take account of the 3D reflections. As expected, the empirical Okumura-Hata does not consider the topography at all. The power delay profiles show the relative strengths of the signal components at different delays. In the first panel the LoS signal component shortest delay is the strongest and the delayed components are around 5 db weaker.

As the receiver moves into the road curve, the LoS component is strongly attenuated whereas the delayed components are received at the same power level. The last power delay profile in Figure 5.

The number of data points considered for each path is written below the path name. If the Mean value is positive, it means that the measured path loss is larger than the predicted path loss. The standard deviation has been calculated assuming a Gaussian distribution of the data. We have applied a color coding to the table.

If the value of the absolute value of the mean plus the standard deviation exceeds 20 db, the entry has been colored red. If it exceeds 12 db but is below 20 db, the color is yellow, and if it is less than 12 db the color is green. The deterministic models using detailed terrain information do not outperform the empirical models.

The two WinProp proprietary models Two-ray empirical and Dominant Path Model are generally over-estimating the path loss by tens of db. This is the result when using the default parameter values.

However, we believe that the physical basis for the Dominant Path model is good, and if a qualified selection of the model parameters had been made, the result for this model would probably have been better.

The deterministic models perform best on the Gausdal 1 path where there were clear LoS. Here the empirical models overestimate the path loss. This is according to theory: The deterministic models calculate the path loss on the most probable path between Tx and Rx, not taking 3D reflections into account.

This will give an accurate estimate on a true LoS path. The measurements that form the basis for the empirical models have received not only LoS signal components, but also the multipath, and therefore the models estimate the path loss to be less than what would be measured on a LoS path. And the other way around: On the Gausdal 3 path, where there were much multipath, the empirical models perform better than the deterministic models.