"WINNER II Channel Models", ver 1.1, Sept

WINNER II D1.1.2 V1.1
⎡
ρ(d) ∝ ⎢e
⎣
d
− MS
∆
⎤
⎥
⎦
d MS
[ ]
m
ρ(0)
Figure 3-6 Dependence of cross-correlation coefficient matrix over distance.
In 3GPP SCM, shadowing fading for links from one MS to different BSs exhibits constant correlation
coefficient equal to 0.5. This correlation does not depend on distances between BSs or their relative
angular positions as seen from MS and therefore it is not layout dependent. Additionally, this property is
estimated from few measurements and therefore it is not considered as being fully representative for
different WINNER scenarios. This phenomenon has also been investigated in WINNER project in some
extent.
Correlation properties of links from the same MS to multiple BSs (inter-site) were investigated in Phase I
of WINNER project [WIN1D54]. The results showed rather high correlation for one measurement route
and quite low for another. The amount of measurement data was limited, so that we could not specify
correlation other than zero.
The inter-site correlation of shadowing fading is also investigated in the literature for the outdoor macrocell
scenarios: in [Gud91],[PCH01] and [WL02], the authors proposed that the inter-site correlation is a
function of the angle between BSs directions being seen from the MS (θ ); while in [Sau99] author
studied the dependence of the inter-site correlation on the distance between BSs, d . Although some
correlations could be found in the references afore, the results could not show clear correlation behaviour
between different BS:s. Although we also believe that such correlation most probably exists in many
scenarios, at least between Base Stations near each other, at this point we decided to let the correlation be
modelled as zero.
Inter-correlations between links of one MS to multiple sectors of the same BS could be analyzed in a
similar way, by treating different sectors of the BS as independent one-sector BSs. As a matter of fact, the
links from two different sectors to an MS are correlated so that the LS parameters for the links are the
same.
Correlation of large-scale parameters (LSPs) is achieved by using wighed sums of independent Gaussian
random processes (IGRP). If i-th LSP, s , have distribution that differs from Normal (Gauss), required
distribution is generated by applying mapping from random variable
i
BS
s~
i having Gaussian distribution.
Random variable s~
i
will be referred as transformed LSP (TLSP). Prior of mapping s~
i to s
i
, s~
i is
correlated with TLSPs s~
j , belonging to other LSPs or different links (being at certain distance - for
system level correlations). Process applied to introduce or to calculate correlations (from measured data)
is illustrated in Figure 3-7.
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