Rf Guideline Model Calibration

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<p>RF Guideline Propagation Model Tuning</p> <p>RF Guideline Propagation Model Tuning In ASSET</p> <p>Author: Doc-ID: Date:</p> <p>H.H. Rhrig Lucent Technologies Proprietary RFET-QA-REP-00-010-V01.00 12 September 00 Use Pursuant to Company Instructions</p> <p>Revision: Page:</p> <p>1.1 1 of 53</p> <p>RF Guideline Propagation Model Tuning</p> <p>Document History Version 1.0 Date 06/01 Author(s) Hans-Hubert Rhrig Change Description First draft.</p> <p>Author: Doc-ID: Date:</p> <p>H.H. Rhrig Lucent Technologies Proprietary RFET-QA-REP-00-010-V01.00 12 September 00 Use Pursuant to Company Instructions</p> <p>Revision: Page:</p> <p>1.1 1 of 53</p> <p>RF Guideline Propagation Model Tuning</p> <p>CONTENTS1 2 3 INTRODUCTION ................................ ................................ ................................ ............................. 4 WHY TUNING A PROPAGATION MODEL? ................................ ................................ ................... 5 IN COMMON USE PROPAGATION MODELS ................................ ................................ ................ 6 3.1 OKUMURA-HATA-MODEL................................ ................................ ................................ .............. 6 3.2 COST231-HATA MODEL ................................ ................................ ................................ ............. 7 3.3 RACE-1043 CLUTTER MODEL ................................ ................................ ................................ ..... 8 3.4 EXTRA DETERMINISTIC METHODS ................................ ................................ ................................ . 9 3.4.1 Common in use Knife-edge Diffraction Methods ................................ ................................ . 9 3.4.2 Effective Antenna Height Calculation................................ ................................ ................ 11 4 INDICATORS OF PREDICTION M ODEL PERFORMANCE ................................ .......................... 12 4.1 BASIC STATISTICS ................................ ................................ ................................ ..................... 12 4.2 PREDICTION ERROR STATISTICS OF AIRCOM INTERNATIONAL ASSET ................................ .............. 13 4.2.1 Displaying Prediction Error in the 2D-View ................................ ................................ ...... 13 4.2.2 Displaying Received Level/Prediction Error vs. Log(d)................................ ...................... 14 4.2.3 Asset Analyse Text File ................................ ................................ ................................ ... 15 4.3 PREDICTION ERROR STATISTICS OF MEAANALYSE................................ ................................ ........ 16 4.3.1 MeaAnalyse output file _Summary.txt ................................ ................................ ......... 16 4.3.2 MeaAnalyse feature StandardDeviationVsMeanError ................................ ....................... 18 4.3.3 MeaAnalyse feature(s) ...versus Distance ................................ ................................ ........ 19 5 INPUT DATA ................................ ................................ ................................ ................................ . 20 5.1 MAP DATA................................ ................................ ................................ ................................ 20 5.1.1 Paper Maps ................................ ................................ ................................ ..................... 20 5.1.2 Topographical Database ................................ ................................ ................................ .. 21 5.2 CW SURVEY DATA ................................ ................................ ................................ .................... 22 5.3 START PARAMETER VALUES OF PROPAGATION MODEL ................................ ................................ . 23 5.3.1 Aircom ASSET Standard Macrocell Model ................................ ................................ ....... 23 5.3.2 Classification of Hata Adjustment Coefficients to ASSET k-parameter.............................. 24 5.3.3 Enhancement of the ASSET Standard Macrocell Model................................ ................. 25 Clutter Category................................ ................................ ................................ ............................. 27 6 WHICH COEFFICIENTS ARE TUNABLE? ................................ ................................ ................... 28 6.1 ADJUSTMENT COEFFICIENTS OF HATA-MODELS ................................ ................................ ............ 28 6.1.1 Intercept C1 and Frequency Coefficient C2 ................................ ................................ ....... 29 6.1.2 Base Station Heights Adjustment Coefficients C3 ................................ ............................. 30 6.1.3 Path Loss Slope................................ ................................ ................................ ............... 31 6.1.4 Mobile Antenna Height Correction ................................ ................................ .................... 36 6.1.5 Clutter Adjustment L C ................................ ................................ ................................ ....... 37 6.1.6 Diffraction Loss LD and Adjustment Coefficient C6 ................................ ............................ 38 7 THE CALIBRATION PROCESS ................................ ................................ ................................ .... 39 7.1 SORT CW MEASUREMENT DATA ................................ ................................ ................................ 40 7.2 F IRST CW MEASUREMENT ANALYSIS ................................ ................................ .......................... 40 7.3 F IND BEST SUITED EFFECTIVE ANTENNA HEIGHT CALCULATION METHOD ................................ ........ 40 7.4 T UNE BASE STATION HEIGHT ADJUSTMENT COEFFICIENTS ................................ .............................. 41 7.4.1 Tune base station height adjustment coefficient k5 ................................ ........................... 41 7.4.2 Tune base station height with distance adjustment coefficient k6 ................................ ...... 42 7.5 T UNE INTERCEPT AND SLOPE COEFFICIENT ................................ ................................ .................. 42 7.5.1 Intercept ................................ ................................ ................................ .......................... 43 7.5.2 Slope ................................ ................................ ................................ ............................... 44 7.5.3 Near/Far Intercept and Slope Coefficients ................................ ................................ ........ 44Author: Doc-ID: Date: H.H. Rhrig Lucent Technologies Proprietary RFET-QA-REP-00-010-V01.00 12 September 00 Use Pursuant to Company Instructions Revision: Page: 1.1 2 of 53</p> <p>RF Guideline Propagation Model Tuning</p> <p>7.6 7.7 7.8 7.9 8</p> <p>T UNE CLUTTER OFFSETS................................ ................................ ................................ ........... 45 F IND BEST SUITED KNIFE-EDGE DIFFRACTION METHOD ................................ ................................ ... 46 T UNE THE DIFFRACTION ADJUSTMENT COEFFICIENT................................ ................................ ....... 46 REANALYZE, F INE TUNING ................................ ................................ ................................ .......... 47</p> <p>HOW TO USE MEAANALYSE ................................ ................................ ................................ ...... 47 8.1 8.2 8.3 8.4 GET THE BIN INFORMATION ................................ ................................ ................................ ........ 47 GET SPREAD SHEETS WITH MEAANALYSE ................................ ................................ ................... 50 CREATE CHARTS BY EXCEL ................................ ................................ ................................ ........ 50 CREATE CHART STANDARD DEVIATION VS. MEAN ERROR IN EXCEL ................................ ................ 52</p> <p>9</p> <p>MATH BASICS ................................ ................................ ................................ .............................. 55 9.1 9.2 STATISTIC BASICS ................................ ................................ ................................ ..................... 55 LOGARITHMIC BASICS ................................ ................................ ................................ ................ 56</p> <p>Author: Doc-ID: Date:</p> <p>H.H. Rhrig Lucent Technologies Proprietary RFET-QA-REP-00-010-V01.00 12 September 00 Use Pursuant to Company Instructions</p> <p>Revision: Page:</p> <p>1.1 3 of 53</p> <p>RF Guideline Propagation Model Tuning</p> <p>1</p> <p>Introduction</p> <p>To implement a mobile radio system, wave propagation models are necessary to determine propagation characteristics for any arbitrary installation. Predictions are required for a proper coverage planning, for interference analysis as well as for cell calculations, which are the basis for the RF network design and optimization purposes. However, the radio propagation channel is a very critical component for mobile radio communications systems. The field strength level, at a given point, not only depends on its distance from the transmitter, the frequency of transmission and the antenna heights but also on the long-term and short-term interferences caused by reflections of the natural environment (terrain configuration, vegetation) and the man-made environment. This influences the wave propagation in different ways. Well-known empirical path loss prediction models like the model of Okumura -Hata or the COST231Hata model estimates the median signal strength in a small area and do not consider the path specific propagation effects by detailed analytical expressions. The Hata models (or other empirical methods) only use simple empirical expressions extracted from curves get from the analysis of measurement data. This has the advantage of implicitly taking all path specific propagation effects of the environment (known or unknown) into account mentioned above. However, each region or country and in the end each city has the own specific character of topography, vegetation and man-made structure have an effect on the wave propagation. Therefore, empirical models must always be subjected to stringent validation by testing it on measurement data sets collected at locations and conditions (as well as at transmission frequencies) which are in many cases other than used to produce the model in the first place. The overall objective of the tuning process is to adapt the propagation model to the local environments characterized by CW measurement data, in conjunction with the specific classification of the actual terrain database. But a tuned propagation model is only good as the input data used to calibrate it. Consequently, the results of the tuning process depends on quality and quantity of the CW measurements, on the quality of used terrain database as well as on the ability of the RF Planning tool to support the user with suitable applications to the CW measurement analysis process. Furthermore, the person who will carry out the tuning process should have knowledge about the basic mathematics and the basic wave propagation mechanism in different mediums as well as knows the common in use propagation models, effective antenna height calculation as well as knife-edge diffraction methods. The purpose of this paper is not to describe the perfect way of tuning empirical propagation models, because there is no single correct way or ideal method. This paper tries to give recommendations and methods useful for the tuning process with help of the Aircom Asset CW Measurement Analyse Tool.</p> <p>Author: Doc-ID: Date:</p> <p>H.H. Rhrig Lucent Technologies Proprietary RFET-QA-REP-00-010-V01.00 12 September 00 Use Pursuant to Company Instructions</p> <p>Revision: Page:</p> <p>1.1 4 of 53</p> <p>RF Guideline Propagation Model Tuning</p> <p>2</p> <p>Why tuning a propagation model?</p> <p>The overall object of tuning a propagation model is to adapt the path loss prediction model to the local environments and the specific classification of the actual terrain database to improve the coverage estimation (path loss prediction). Because: y Each region or country has the own specific character of vegetation and man -made structure that influence the wave propagation on different ways. The Hata models based on the Okumura technique adopts curves for urban areas based on the type and density of buildings in Tokyo and it may not be transferable to cities in Europe or North America. Indeed, experience with CW measurements in the USA (e.g. South Carolina, Indianapolis and Boston) have shown that the typical US urban environment lies is similar to Okumuras definition of suburban. Empirical path loss prediction models like the COST231-Hata model (see next chapter) are restricted to flat terrain. In case of wavy (hilly) terrain or topographical obstacles like mountains (obstruct the line of sight between BS and MS) the Hata model has to combine by extra deterministic methods have to use like knife-edge and/or effective antenna height calculation to consider the influence of topography. Usually empirical models are restricted to ranges of frequencies, antenna heights and distances. If the parameter of the planned base stations are outside these limitations, then the empirical model have to extent by analyzing CW measurements. High-resolution terrain databases (e.g. pixel size is from 20 meter up to 30 meter) are created by satellite images (typical 10 meter resolution). However, the clutter database is the result of a person, who interprets groups or cluster of gray-pattern in the image and assign the marked area to the most likely suitable clutter category. Furthermore, a satellite image provides geo information about the local density and extent of buildings, but it cannot give information about the local building heights that also impact on wave propagation. Consequently, the path loss prediction has to adapt to the topographical database by the help of CW measurements. Some RF planning tools support extra clutter attributes like clutter heights and separation. Using these features can improve the accuracy of the coverage prediction. It is recommended to validate the specified clutter information by CW measurements.</p> <p>y</p> <p>y</p> <p>y</p> <p>y</p> <p>Note:</p> <p>Keep in mind, that the fitted propagation model is only applicable to the local terrain database that was used for the model tuning.</p> <p>Author: Doc-ID: Date:</p> <p>H.H. Rhrig Lucent Technologies Proprietary RFET-QA-REP-00-010-V01.00 12 September 00 Use Pursuant to Company Instructions</p> <p>Revision: Page:</p> <p>1.1 5 of 53</p> <p>RF Guideline Propagation Model Tuning</p> <p>3</p> <p>In Common use Propagation Models</p> <p>This chapte...</p>