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Final report on link level and system level channel models - Winner

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WINNER D5.4 v. 1.4<br />

The parameters for scenario A1, B3 <strong>and</strong> D1 have been obtained from the WINNER measurements<br />

described in Secti<strong>on</strong> 5.2.1, 5.2.3 <strong>and</strong> 5.2.6. For scenario B1, reference data from TKK outside the<br />

WINNER project is used. A combinati<strong>on</strong> of the measurements described in Secti<strong>on</strong> 5.2.4 <strong>and</strong> literature,<br />

Secti<strong>on</strong> 5.5.4, is used for scenario C1. For scenario C2, results from the reference measurement data<br />

measured by Nokia outside the WINNER project <strong>and</strong> literature (see Secti<strong>on</strong> 5.5.5) is used.<br />

For scenario A1 LOS/NLOS, C1 LOS/NLOS, C2 NLOS, D1 LOS/NLOS <strong>and</strong> Bridge2Car, all parameters<br />

are modelled as log-normal, which means that the transformati<strong>on</strong> is simply “log(x)” <strong>and</strong> the inverse<br />

“10^x”. One excepti<strong>on</strong> is the shadow fading, where the transformati<strong>on</strong> is “10log(x)”, so that the<br />

transformed variable is in dB. In scenario B1 LOS, the delay-spread is log-Gumbel, while the AoD <strong>and</strong><br />

AoA spread is log-Logistic. In B1 NLOS, the delay-spread, AoD <strong>and</strong> AoA spread are all log-Gumbel.<br />

The transformati<strong>on</strong>s for these cases are described in Secti<strong>on</strong> 3.1.1. In scenario B3, the delay-spread, AoD<br />

spread, <strong>and</strong> AoA spread are modelled as normal (thus the transformed <strong>and</strong> untransformed variables are<br />

the same). In all cases, the shadow fading is modelled as log-normal.<br />

The mean µ <strong>and</strong> covariance matrix R ( 0)<br />

have been obtained for each scenario <strong>and</strong> are listed in the tables<br />

of Secti<strong>on</strong> 3.1.1. Also listed in Secti<strong>on</strong> 3.1.1 for each scenario <strong>and</strong> parameter is a decorrelati<strong>on</strong> distance<br />

∆ . This distance has been obtained by fitting a single exp<strong>on</strong>ential exp( − ∆r / ∆)<br />

to the auto-correlati<strong>on</strong><br />

functi<strong>on</strong>s. The expressi<strong>on</strong> (*) however, mixes all of the decorrelati<strong>on</strong> distances λ<br />

1,<br />

K,λm<br />

so that we need<br />

to do a joint fit of all four auto-correlati<strong>on</strong> functi<strong>on</strong>s. Below, we have plotted the exp<strong>on</strong>ential<br />

exp( − ∆r / ∆)<br />

together with the auto-correlati<strong>on</strong> obtained from (*). The decorrelati<strong>on</strong> distances<br />

λ<br />

1,<br />

K,λ m have been manually optimized <strong>and</strong> their values are listed in Table 3.4.<br />

5.4.13.1 A1 LOS<br />

Figure 5.70: The auto correlati<strong>on</strong> functi<strong>on</strong>s obtained from (*) using the λ parameters of Table 3.4<br />

<strong>and</strong> the single exp<strong>on</strong>ential functi<strong>on</strong>s obtained from measurements in Scenario A1 LOS.<br />

5.4.13.2 A1 NLOS<br />

Page 106 (167)

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