Model Tuning

Why we need a Model ? We need a propagation model for :

•To determine coverage levels and areas covered by a site/sites.

•Calculate the levels of interference (C/I, C/A) within the network.

•Determine the traffic distribution in the network.

Different types of models : •

Okumura - Hata.

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Walfisch - Ikegami.

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General Model.

Other Models. •

Micro - Cells.

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Two Part Models.

Parameters controlling the propagation model will be discussed in the following slides.

Okumura-Hata General propagation model The received signal strength at the mobile is given by the following equation:

Receive power in dBm.

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PRX

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PTX

Transmit power (EiRP) in dBm including Ant Gain.

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K 1

Constant offset in dB. (Intercept power value)

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K 2

Slope describing loss as a function of distance( log(d).)

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K 3

Describing loss as a function of the BTS effective ant height log (H eff).

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K 4

Constant describing the loss as a function of diffraction.

• K 5

Okumura-Hata joint function of BTS effective ant. height and the distance log(Heff) log(d) .

• K6

Correction factor for the mobile effective antenna height gain (Hmeff) .

•d

Distance, in meters, between MS and BTS.

• Heff

BTS effective antenna height.

• KCLUTTER

Gain or Loss in dB for clutter type at mobile position.

• Hmeff Mobile effective antenna height.

Tuning a Model Tuning of RF models is the most important part of predicting the RF receive levels in a cellular system since this is the tool that calculates the path loss between Base station and Mobile stations.

To understand the tuning process, it is important to understand that the RF models are empirically derived from RF propagation data measured (CW).

The process of tuning a model involves fitting measured survey data in an area to selected model parameters that allows the model to yield values that closely fit the measured survey data (Curve fitting).

This process includes several steps that are critical :

•Selection of proper models

• Availability and verification of survey data

•Selecting the best of K1 and K2 values to fit survey data

• Assigning proper weighting to clutter &

•Using a systematic process to select values for the remaining model parameters Like K3, K4, K5, K6 & Kclutter.

Pre-requisites for Model Tuning Mapping data validation, i.e. the clutter and terrain database that is used to derive the propagation models should be thoroughly checked.

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Ensure that clutter, heights and vector data uses the same mapping projection.

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CW survey site selection should be approx. 10% to 15% of the system count.

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It is very important that the selected sites covers all clutter categories for the ability to calculate the clutter factors.

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Things to remember when selecting the sites to survey

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It is important to target hilly as well as flat areas.

The CW survey sites should have variations in the effective antenna height to enable PLANET to model the height dependence factor.

CW test sites should provide enough radio clearance for the survey Tx antenna.

Planning Measurement Routes Measurement routes should be prepared split into zones  circle around the survey site

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Max range for survey routes as a circumference app. 3  3.5mi for Urban/Suburban areas and approx. 6-9mi for rural areas.

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Survey routes should be planned to avoid radial routes with line of site from the transmitter but provide a mixture of wide and narrow streets that have the tangential and arbitrary orientation with respect to the base station.

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Basically the routes should be a representation of all clutter categories in the terrain database.

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Before measurements ensure CW equipment, feeder, and antenna are calibrated.

Things the survey team should do during measurement :

•Ensure the CW receiver noise floor is not reached. •Determine if line of sight or major shadowing conditions exist when signal increases/decreases by 10-15dB. •Monitor and record on the map and CW survey report form unrepresentative conditions such as tunnels, flyovers and bridges.

• After completion CW log files loaded into PLANET using the Survey Loader. • A header file for each log file is created. •Spatial filtering is then carried out to remove data points from close into the transmitter since this will not contribute to distance dependence and to remove data points close to noise floor having max range. •Signal filtering is carried out to ensure receiver linearity. •Unrepresentative data points such as tunnels, bridges, large shadowing features, etc., should be deleted.

Try to analyze each log file for every site and try to draw the signal levels for each survey file loaded and identify the unwanted log files closer to the noise floor(-120dBm) and remove from the files for tuning before combining into a single final file for tuning.

Once above said all is taken care calibration of propagation should commence.

The tuning of the model involves the determination of diff coefficient values and clutter factors (K1 thru K6) such that the residual RMS (difference between measured and predicted signal strength) value is set to its lowest value. This is not an easy task and takes a number of iterations before succeeded.

The Received Signal In A Real World. Underlying Fall In Level Proportional to 40log(d). Slow Variations Caused by Terrain.

   l   e   v   e    L    l   a   n   g    i    S Fast, Deep Fades Caused By Reflections

Distance From Transmitter

Propagation over plane earth would give us received signal level Proportional to 40log(d) – Shown in red Effects of terrain shadowing gives us a signal level with log-normal Distribution about theoretical level and a std. deviation of 8dB  – Shown in blue Effects of reflections gives us a signal level with Raleigh distribution with Fading depths over 40 dB. Sum of above 3 effects is shown in black

General Principles. Intercept Offsets Caused By Clutter etc.

   l   e   v   e    L   e   v    i   e   c   e    R

Slope

Distance from Base Station

Models are generally based on the principle that the level (measured in dB) falls in a linear fashion with distance from the transmitter. This is represented by a term in the model of K2.log(d) where K2 is the slope.  At some distance from the transmitter the level is set to a fixed value known as the intercept K1.  An offset may be applied for effective base station antenna height or mobile effective antenna height all along the path.  “Local” offsets may be applied to the model at different points to reflect the effects of different clutter types at different points along the path or the

Path Loss Slope.    )   m    B    d    (    l   e   v   e    L    d   e   r   u   s   a   e    M

Distance from Base Station

The diagram represents a number of signal level measurements taken at various points within the coverage area of a cell. It is possible to draw a straight line through this plot that will sho the underlying slope of the level/distance characteristic.

To test the accuracy of the line that has been drawn it is necessary to calculate the error at every measurement point and hence a mean error. If the line that had been drawn was the blue one instead of the red one there is obviously an error. If the mean error is calculated, because there are both positive and negative errors, it will come to zero. To test the slope, therefore, the RMS error must be calculated.

The slope of the line is now fixed.

Path Loss Intercept.    )   m    B    d    (    l   e   v   e    L    d   e   r   u   s   a   e    M Distance from Base Station

It is possible to move the line up or down on the plot. If this is done and the mean error, between the line and the actual measurements, is calculated it is possible to place the line so that it is close to zero mean error. The diagram shows a red line with the correct offset and a blue line with an incorrect offset.

It is now possible to mark the plot at a fixed distance from the base station and to obtain a value in dBm for the intercept point. This point is shown marked in green on the diagram. The slope and intercept values have now been calculated and may be used in the propagation model.

Clutter Values.

   )   m    B    d    (    l   e   v   e    L    d   e   r   u   s   a   e    M

The local variations in level may be due to clutter at the mobile location. Samples have been color coded to indicate the type of clutter present at each sample site.

Distance from Base Station

Having assigned clutter values, the model must be run and its predictions compared with the real measurements. The calculation of mean errors in different types of clutter and the standard deviation of errors enables these values to be fine tuned. There is also an overall clutter weighting to be assigned.

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Create a new model.

First make all the clutter factors equal to Zero.

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Choose General

Start tuning the model by playing with Intercept K1 and Slope K2.

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Now try to bring the straight line on Log(d) Vs Error to zero so that mean error distribution is even on both sides of bell shaped Gaussian curve

Once you have made Log(d) Vs Error zero.  Apply the clutter Factors for the model.

 Again play with K1 &K  till std. dev is reduced to (8-9)dB.

Errors And Accuracy. •

Topographical Data.

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Clutter Data.

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Vector Data.

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Level Measurements.

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Navigation.

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The Overall Picture.

 Vector data errors :-

Errors in topographic data might be out of date ground surveys, wrong interpretation of airborne photograph surveys, low resolution space borne surveys and also errors introduced in digitizing maps (DTM). Mapping companies may wrongly classify clutter data. The border between two very different types of clutter is therefore difficult to define and prediction errors of several dBs can result. (Spatial accuracy around clutter Borders).

 Vector data such as roads may be missing from some maps or out of date on others. The roads most likely to be missing are, of course, those most likely to generate large volumes of traffic.  As in all areas of mapping, data may be incorrect, either in the original source documents or as a result of an error in digitization.

Level Measurement errors :CW Survey equipment should be calibrated. EIRP of a Base station can be very difficult to verify with in a few dB because its not possible to measure the installed gain and radiation pattern of an antenna. Navigational errors:Navigation systems carried by survey vehicles must be kept in calibration and checked on a regular basis. The Overall picture :Survey data can be in error. Mapping data can be in error. Propagation is stochastic process and so we cannot expect to find every measurement to be representative of what is really happening.

The Limitations Of Predictions. •

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Propagation is A Stochastic Process which is determined by a random distribution of probabilities. Any Description of Propagation Must Be Statistical and Not Deterministic

It is not possible to say that the receive level at a particular point is XdBm. It is however possible to say that there is a 98% probability that the level is within YdB of XdBm.

In General it can be expected that the level measured at a particular point will have a log. normal distribution with a mean as predicted by the model and a standard deviation of 8dB. It must be remembered that there is also fast Raleigh distributed fading superimposed on top of this which indicates that there is a 1% probability of the received level falling a further 20dB.

Probability and Planning •Propagation is a random process and it is impossible to say that the received level at a particular point coming from a particular transmitter is an absolute level. •The resulting signal level is a random variable, all we are able to do is to make statements such as “there is a 98% probability that the received level will be within 8dB of an absolute value. A random variable does not have an absolute value. To be able to make this statement we must have a mathematical model that is accurate enough to do this. That is to say that somebody has made a large number of field measurements of receive levels and, from a theoretical base, has constructed a mathematical model, the output of which matches those measurements. – Definition of a propagation model.