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A17.19.1 Description of the model

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Table A19.2: LOS Probability


Scenario

LOS probability

C2

PLOS=min18d, 1⋅1-exp-d63+exp-d63

P_{LOS}=\min{

Mathinline
body--uriencoded--P_%7BLOS%7D=\min%7B\left(\

frac{18}{d}

frac%7B18%7D%7Bd%7D,\ 1\right)

}

%7D\cdot\left(1-\

exp{

exp%7B\left(-\

frac{d}{63}

frac%7Bd%7D%7B63%7D\right)

}

%7D\right)

{

%7B+

}

%7D\

exp{

exp%7B\left(-\

frac{d}{63}

frac%7Bd%7D%7B63%7D\right)

}

%7D

C1

PLOS=exp-d200

D1

PLOS=exp-d1000

Mathinline
body--uriencoded--P_%7BLOS%7D=\exp%7B\left(-\frac%7Bd%7D%7B200%7D\right)%7D

D1

Mathinline
body--uriencoded--P_%7BLOS%7D=\exp%7B\left(-\frac%7Bd%7D%7B1000%7D\right)%7D


Wiki Markup
Note: System level simulations require estimates of the probability of line-of-sight. The LOS probability models are based on relatively limited data sets and/or specific assumptions and approximations regarding the location of obstacles in the direct path, and should therefore not be considered exact.
\\
In case the LOS probability is used, the path loss is computed as follows \[2\]:
\\



PLProbdB=PLOS⋅PLLOSdB+1-PLOS⋅PLNLOS\[dB\]




\\

Mathinline
body--uriencoded--PL_%7BProb%7D\left[dB\right]=P_%7BLOS%7D\cdot PL_%7BLOS%7D\left[dB\right]+\left(1-P_%7BLOS%7D\right)\cdot PL_%7BNLOS%7D[dB]

where path losses in the LOS and NLOS conditions (PLLOS and PLNLOS) and the LOS probability, PLOS, for a given scenario are shown in Table A.17.19.1 and Table A.17.19.2, respectively.


A17.19.2 Input parameters

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Figure A17.19.1: GUI of the WINNER II path loss model


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Table A.17.19.3: Parameters of the WINNER II path loss model


Description

Symbol

Type

Unit

Comments

Variation

σSF

B

dB

Variation in path loss (applies shadow fading)

Scenario

-

S

-

Urban Macro Cell (C2), Suburban Macro Cell (C1) or Rural Macro Cell (D1)

Line of Sight

-

S

-

Line of Sight (LOS), Non-Line of Sight (NLOS), or LOS Probabilities

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