The comparison of methods for constructing the radio frequency layer of radio environment map

using participatory measurements

Marko Pesko1, Tomaž Javornik2, Mitja Štular1, Mihael Mohorčič2

1 Telekom Slovenije, d.d., Ljubljana, Slovenia

2 Jozef Stefan Institute, Ljubljana, Slovenia

4th Workshop of COST Action IC0902

Rome, Italy, October 9-11, 2013

Outline

�  Motivations �  Related work �  Improving RF-REM construction �  Self-tuning construction method �  RF-REM area of interest �  Participatory measurement sets �  The performance metric �  Results �  Conclusions

2

Motivations

�  RF-REM construction is so far mostly performed by direct interpolation-based methods

�  Spectrum measurements are nowadays obtained from dedicated sensing deployments or measurement campaigns

�  The future radio environment is expected to be more dynamic and participatory sensing could replace dedicated sensing

�  The performance of current and to participatory measurements adapted RF-REM construction methods must be evaluated

3

Related work

�  Recently proposed indirect method LIvE shows great potential of indirect methods: ¡  H. B. Yilmaz and T. Tugcu, “Location Estimation-Based Radio

Environment Map Construction in Fading Channels”, Wireless communications and mobile computing, 2013.

¡  Test scenario: ÷ Grid-based sampling, omnidirectional transmitter, assumed known

information of channel parameters, log-normal shadowing and Rayleigh fading

÷ „The simulation results suggest that the location estimation based REM construction outperforms the compared methods…“

¡  Consideration of operating environment and Tx characteristics could further improve such indirect RF-REM construction

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Improving RF‑REM construction

�  Making a step further, we developed a new indirect RF‑REM construction method to consider: ¡  the operating environment, ¡  transmitter parameters, including the antenna pattern, ¡  calibration of the selected propagation model

�  An indirect self-tuning method (STM) for RF-REM construction: ¡  M. Pesko, L. Benedicic, T. Javornik, A. Kosir, M. Stular, M.

Mohorcic “An indirect self-tuning method for constructing the radio frequency layer of radio environment map”, submitted to IET Electronics Letters in August 2013, under review and subject to Institution of Engineering and Technology Copyright.

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Self-tuning construction method

�  Estimted signal strength on i-th RF-REM location: ÷ 𝑅𝐹‑𝑅𝐸𝑀↓𝑖 = 𝑃↓𝑇𝑥 − 𝐿↓𝑅𝐸𝑀↓𝑖  = 𝑃↓𝑇𝑥 −(𝐿↓𝑖 + 𝐿↓𝑑𝑖𝑓𝑓↓𝑖  + 𝐿↓𝑐𝑙𝑢𝑡↓𝑖  − 𝐺↓𝑎𝑛𝑡↓𝑖  )

�  where: ÷  𝐺↓𝑎𝑛𝑡↓𝑖  = 𝐺↓𝑚 −𝐹𝐵𝑅+𝐹𝐵𝑅| cos↑n  ( 𝛩↓0 − 𝛩↓𝑖 /2 )  | 

÷  𝐿↓𝑖 = 𝐴↓0 + 𝐴↓1 log↓10  (𝑑)+ 𝐴↓2  log↓10  (𝐻↓𝑒𝑓𝑓 )  +   𝐴↓3 log↓10  (𝑑)log↓10  (𝐻↓𝑒𝑓𝑓 )     

                       −3.2   [log↓10  (11.75   𝐻↓𝑚 ) ]↑2 +  44.49  log↓10  (𝑓)−4.78  [log↓10  (𝑓) ]↑2  

÷  F(𝛽 ,𝑗)= 1/𝑁 ∑𝑖=1↑𝑁▒(𝑃↓𝑖 −(𝑃↓𝑇𝑥 − 𝐿↓𝑅𝐸𝑀 ↓𝑖 (𝛽 (𝑇↓𝑗 ))))↑2    ÷  𝛽   =[𝐴↓0 ,   𝐴↓1 ,   𝐴↓2 , 𝐴↓3 , 𝐺↓𝑚 ,𝐹𝐵𝑅, 𝛩↓0 ,  𝑛] (𝛽 ↑∗ , 𝑗↑∗ )=𝑎𝑟𝑔𝑚𝑖𝑛  F(𝛽 ,𝑗)

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RF-REM area of interest

�  The size of area: 5.15 km by 6.75 km with resolution of 25 m

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¡  9 d i f f e r e n t c l u t t e r categories ÷ Dense urban area ÷ Urban area ÷ Urban area wi thout

buildings, mostly roads ÷ Suburban area ÷ Dry open land without

special vegetation ÷ Agricultural area ÷ Forestall area ÷ Swamp area ÷ Water area

Participatory measurement sets

�  GRASS-RaPlaT + information from mobile operator‘s disposal ¡  real BS transmitter information (location, antenna pattern, Tx power) ¡  digital elevation model (DEM) ¡  clutter map

�  Matlab: ¡  Sampling of such RF‑REM and formation of

measurement sets of different sizes ÷ Multiple measurement sets of the same size

(location dependency)

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+ Rayleigh fading

Methods‘ performance metric

�  RF‑REM performance metrics from literature: ¡  mean squared error (MSE) ¡  mean absolute error (MAE) ¡  relative Mean Absolute Error (RMAE) ¡  root mean square error (RMSE) ¡  FAZR and CDZR (introduced by Yilmaz) ¡  etc.

�  The selected performance metric is: ¡  RMSE   

÷  i.e. average of RMSE values calculated between „TRUE“ and reconstructed RF‑REMs for different measurement sets of the same size

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Results

�  The considered RF‑REM construction methods: ¡  IDW ¡  IDW2 ¡  Kriging ¡  LIvE ¡  STM

�  Preliminary results of the considered construction methods comparison

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50 100 150 200 250 300 350 400 450 5002

4

6

8

10

12

14

16

18

20

Measurements set size

RM

SE

IDW LIvE IDW2 Kriging STM

The same methods as in LIvE test scenario

Preliminary results 11

101

102

103

0

5

10

15

20

25

30

35

40

45MCDs distribution: random

Number of measurements N

RM

SE

IDWIDW2KrigingLIvESTM:NoAntennaSTM:Antenna

101

102

103

0

5

10

15

20

25

30

35

40

45MCDs distribution: all in main lobe of Tx antenna

Number of measurements N

RM

SE

IDWIDW2KrigingLIvESTM:NoAntennaSTM:Antenna

101

102

103

0

5

10

15

20

25

30

35

40

45MCDs distribution: 95% in main lobe of Tx antenna

Number of measurements N

RM

SE

IDWIDW2KrigingLIvESTM:NoAntennaSTM:Antenna

101

102

103

0

5

10

15

20

25

30

35

40

45MCDs distribution: clusters (clutter)

Number of measurements N

RM

SE

IDWIDW2KrigingLIvESTM:NoAntennaSTM:Antenna

101

102

103

0

5

10

15

20

25

30

35

40

45MCDs distribution: real traffic (distances from Tx)

Number of measurements N

RM

SE

IDWIDW2KrigingLIvESTM:NoAntennaSTM:Antenna

101

102

103

0

5

10

15

20

25

30

35

40

45MCDs distribution: 95% outside main lobe of Tx antenna

Number of measurements N

RM

SE

IDWIDW2KrigingLIvESTM:NoAntennaSTM:Antenna

Conclusions

�  RF‑REM performance evaluation results were presented for several existing methods in parallel with the performance of the STM method

�  All methods were tested on the same sets of spatially distributed participatory measurements and compared in terms of RMSE   

�  The results confirm that the accuracy of the RF‑REM can be notably enhanced by construction methods considering the operating environment, propagation model tuning and transmitter characteristics

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The research leading to these results has been partially funded by the European Union, European Social Fund and the FP7 projects ABSOLUTE (FP7 ICT-318632) and CREW (FP7-ICT-258301).

Acknowledgements 13

Thank you!

Marko Pesko marko.pesko@telekom.si

4th Workshop of COST Action IC0902

Rome, Italy, October 9-11, 2013

4th Workshop of COST Action IC0902

Rome, Italy, October 9-11, 2013

GRASS-RaPlaT

¡  Developed by JSI (http://comms.ijs.si/en/software/grass-raplat) ÷ Open-source radio coverage simulation tool based on GRASS with user

extendible set of radio propagation models ÷ For research and professional communication network planning

¢  modules for a number of channel models ¢  module for sectorisation according to given antenna radiation patterns ¢  module for calculating and

storing the complete radio network coverage data

¢  supporting modules (e.g. for adapting input data and analyzing simulation results)

÷ The accuracy has been validated on existing real GSM network data

16

Celllist

DEM Land  usage[dB]

Antennadiagram

Attenuation

r.sector

r.MaxPower

Cell  attenuation

Maximalsignal

Power  table  (dbf)

db.GenerateTable

r.fsplr.hatar.cost231r.hataDEMr.waik

Landusage

SimulationsTEMs

r.clutconvert

Land  usagetable  -­‐ dB

db.CompareResults

r.CompareMobitel

Measurements

Resultscomparison

Resultscomparison

Python

Loop

r.compare

Comparison