# Presentation: 689759

## MEASUREMENT DATA FOR IMPROVING ITU-R RECOMMENDATION P.1812Database of filtered measurement records

**RAL**

## Rationale

- The new database of measurements provided to SG3 has proved very useful in evaluating models for the improvement of P.1812
- Inevitably a large database will contain some errors
- The difference between a prediction and a measurement depends on both model and measurement errors
- Comparisons of models against invalid data may give misleading results
- Not all of the data within the database is of equal weight. For example:
- There are some long term measurements and some point samples
- There are some height gain measurements which should be used for height gain, but not for path evaluation
- The database is not uniformly distributed over parameter space of the models.
- Risk of bias if model only matches well to the most numerous measurement set

## Rationale

- It appeared that differences in models were being strongly masked by the variability of the database
- Some measurements were very clearly in error
- Line of sight paths with much less than line of sight loss at 50% time
- The prediction errors were not normally distributed
- I.E. the errors were not random
- Some datasets were missing some parameters
- Clutter profiles
- Low resolution terrain profiles
- Missing radioclimatic data and Land/Sea/Coastal information
- The height gain measurements were adding noise to the sampling
- A lack of associated clutter data meant that nearly identical paths had large path loss differences

## Plan

- The measurement database required closer examination and a plan was made to:
- Filter the records against a set of rules
- Flag each record against several criteria
- Move the records into a new database
- To permit flagging of the data
- To allow missing data to be filled in where possible
- To allow new data from Cost210 and Sandell to be added
- To allow model results to be stored alongside the data records
- Create an extraction application
- Permit data to be extracted based on database queries of parameter values
- Plot and display data and results in a readily usable form
- To help analyse prediction outliers manually

## Data validity

- All measurement records were added into the database and then assessed into three categories
- Records thought to be good
- Records with a few minor concerns but otherwise good
- For example records that were repairable
- Records with major concerns
- For example
- Records where the path profile was not monotonic
- Records with missing vital parameters
- Records with conflicting information
- Dummy records designed for model implementation testing
- Records falling into the first two are considered suitable for testing
- Records were only flagged, none were thrown away

## Flagging 1

- Two major level flags were applied
- IsValid
- Records thought to be good (Flagged as 1)
- Records with a few minor concerns but otherwise good (Flagged as 1)
- For example records that were repairable
- Records with major concerns
-   0 	Test links
- - 1 	TX, RX location concerns
- - 2 	Path profile concerns
- - 3 	Clutter concerns	- depends on our belief in LOS model
- - 4 	Duplicate link
- - 5 	Calibration concerns - depends on our belief in LOS model

## Flagging 2

- IsLongTerm
- Records with many time percentages considered long term (1)
- Records with none or 50% time considered not long term (0)
- This is the majority of the data
- In addition:
-    An “RxHeightGainGroup” flag was added to identify height gain tests
- and
-   The highest RX point is a record is flagged with “IsTopRXHeightInGroup”

## Parameter coverage of the database

- (just a few examples – more in paper)

## Flag counts

| Field and Value |  | # Links (Measurements) |
| --- | --- | --- |
| Total |  | 5832 (35840) |
| IsValid=0 (test links) |  | 8 (24) |
| IsValid=-1 (TX, RX location concerns) |  | 38 (130) |
| IsValid=-2 (profile concerns) |  | 32 (104) |
| IsValid=-3 (clutter concerns) |  | 341 (3226) |
| IsValid=-4 (duplicate link) |  | 19 (27) |
| IsValid=-5 (LOS path loss concerns) |  | 21 (823) |
| IsValid=1 && IsLongTerm=0
(&& IsWorstMonth=0) | Total | 4922 (29061) |
|  | InputsValid=1 | 4914 (25309) |
|  | InputsValid=1 && IsTopRXHeightInGroup=1 | 4914 (9639) |
| IsValid=1 && IsLongTerm=1 && IsWorstMonth=0 | Total | 428 (2307) |
|  | InputsValid=1 | 402 (1410) |
|  | InputsValid=1 && IsTopRXHeightInGroup=1 | 402 (1410) |
| IsValid=1 && IsWorstMonth=0 | InputsValid=1 && IsTopRXHeightInGroup=1 | 5316 (11049) |

## Parameter coverage

- How well are longer and shorter paths and higher and lower frequencies represented?

- Path Lengths

- (Green = Long Term data,  Red = Short term data,  Black = All data)

- Frequencies

## Parameter coverage

- Height above ground

- TX

- (Green = Long Term data,  Red = Short term data,  Black = All data)

- RX

## Joint parameter coverage

**Joint parameter coverage**

- Frequency

- Path Length

## Accessing the data

- (and the model results)

## SQL Database

- Data is stored in an SQL database
- Can output data in XML, KML, SG3 CSV

## Web access

- Link	_[http://www.rcru.rl.ac.uk/njt/linkdatabase/linkdatabase.php](http://www.rcru.rl.ac.uk/njt/linkdatabase/linkdatabase.php)_

- A javascript application facilitates access via the web interface
- Links in to Google maps

## Sample outputs

## Data analysis

- http://www.rcru.rl.ac.uk/njt/linkdatabase/linkdatabase_predictions.php

- Path loss against path length (filtered data)

## Displaying model results

- Typical prediction analysis data extraction filter:
-  DiffModel==1 AND IsTopRXHeightInGroup==1 AND IsValid==1 AND IsWorstMonth==0 AND InputsValid==1

| Field and Value | Diffraction Model |
| --- | --- |
| DiffModel=1 | 3-edge Deygout model as in ITU-R P.1812 |
| DiffModel=3 | Bullington model with LOS taper |
| DiffModel=5 | 3-edge Deygout model as in ITU-R P.1812 with some aspects of the US PTP cylindrical edge model |
| DiffModel=6 | 3-edge Deygout model as in ITU-R P.1812 with Chinese spherical Earth proposal detailed in ITU document 3K/150-E |
| DiffModel=7 | Bullington model with LOS taper and Markus Liniger’s distance correction [9th order polynomial fit of log(path_length) to 3-edge Deygout mean] |
| DiffModel=8 | Bullington model with LOS taper and Markus Liniger’s distance correction as additive term |
| DiffModel=9 | Bullington model with LOS taper and David Bacon’s distance correction [3 point fit to 3-edge Deygout mean]  |

## Benefits of new filtered data sets

- (by way of examples)

## Take two models

- Unfiltered data – prediction errors
- Which model is best is hard to tell – neither appears much good

- Bullington Taper		3-Edge Deygout

## Take two models

- Filtered data – now highlighting the top height measurements, those least likely to suffer terminal clutter

- Bullington Taper		3-Edge Deygout

## Take two models

- Filtered data – now without height gain measurements but highlighting US Plains Data

- Bullington Taper		3-Edge Deygout

- (we draw no conclusion here, but the differences are becoming clearer)

## Regression fits

- It is possible to make regression fits to the data
- Swiss 9 point distance correction to Bullington vs frequency

## Regression fits

- It is possible to make regression fits to the data
- Alternative distance correction to Bullington vs frequency

- Better at higher frequencies

## Data outliers

- (From P.1812)

## Outliers

- Even with filtered data some measurements are still poorly predicted by P.1812

- Good

- Manually looked at all data more than 40 dB out

## Location accuracy

- Some locations are not exact

- Is the supplied profile right?
- We hope so but in this terrain it is very important, especially with low antennas at the receiver

## Location accuracy

- Here we find a 45 dB over predicted loss

- Allegedly, the receiver is 1.5m above ground, just over the brow of a hill, in the middle of a forest a long way from the road.
- This is unlikely – moving the receiver to the top of the hill and re-calculating the profile gave a model loss within 6 dB of the measurement.

## Unknown clutter

- We noticed something odd with US Phase1 records
- Many of these were made at 100MHz using a 68metre tower in Boulder, Colorado.

- Prediction error is a function of Longitude.
- Paths into the  mountains immediately to the West are predicted well.
- Paths in plains to the East have 20 dB more mean loss than the model predicts.

- At 50% time, this can not be a problem with the ducting hm model
- We believe it is clutter loss

## Unrepresented climates

- Paths in the Gulf region are not well modelled
- Ducting occurs for more than 50% of the time

- This path measurement is 60dB higher than the prediction

## What to do?

- Outliers are an issue - We need to note they exist
- If we eliminate all measurements that disagree with the model the model is bound to agree with the remaining data.
- In some cases we can eliminate the measurement:
- Should we not test against Gulf area measurements until we have a ducting model for that climate?
- We can eliminate paths where we know there is clutter contamination
- but what about those we don’t know about?
- For all models, the mean prediction error for some datasets appears abnormal.
- Should we eliminate these data sets?
- Can we equalise the means for evaluating model fit?
- Should we apply a weighting function to distinguish long term and short term measurements

## Finally

- The models so far for all valid data and top heights against path length

- P.1812

- Bull + Taper

- 3E + PTP

- 3E + Sphere

- Bull – ML 9pt

- Bull – DB 3pt