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

WINNER D5.4 v. 1.4
⎡χ
ms1,
c1
χ
ms1,
c2
L χ
ms1,
c6
⎤
⎢
⎥
= ⎢
χ
ms2,
c1
χ
ms1,
c2
L χ
ms1,
c6
A ⎥
(6.3)
⎢ M M O M ⎥
⎢
⎥
⎣χ
ms3,
c1
χ
ms3,
c2
L χ
ms3,
c6
⎦
The pairing matrix can be applied to select which radio links will be generated and which will not.
6.1.3 Generation of correlated large-scale parameters
The system level modelling will introduce some location dependency between the radio links. This is
done by correlated large-scale channel parameters for the radio links. There can be identified five
different cases in the correlation point of view:
1. One MS is connected to two different BS
2. One MS is connected to two different sectors of a single BS
3. Two different MSs are connected to one sector of a BS
4. Two different MSs are connected to two different sectors of a single BS
5. Two different MSs are connected to two different BSs
The radio links in the cases 1 and 5 are non correlated, case 2 is fully correlated and in the cases 3 and 4
the correlation is a function of distance between MSs. Exception is the shadow fading, which is correlated
also in case 1 with a fixed factor.
Currently, the following large-scale parameters to be correlated are:
1. Delay-spread (DES)
2. AoD angle-spread (ASD)
3. AoA angle-spread (ASA)
4. Shadow fading (SHF)
5. AoD elevation spread (ESD)
6. AoA elevation spread (ESA)
? (all of which have
mean zero and variance one) in the positions x
i
, yi
where the mobiles are located. The elements of
?( x, y)
are uncorrelated, see Section 4.1.4.2. However the auto-correlation of is non-zero. More prisecely
the correlation between element c of the ?( x, y)
vector, i.e. ?
c
( x,
y)
, in two points x , y 1 1 and x , y 2 2 is
given by
The first step is to generate the vector of four real-valued Gaussian variables ( x, y)
E
⎛
2
2 ⎞
⎜ ( x1
− x2)
+ ( y1
− y2)
( = −
⎟
1 1 c 2 2
exp
(6.4)
⎜
λ ⎟
⎝
c
⎠
{ ξ x , y ) ξ ( x , y )}
c
To obtain these values for the K links between a base-station and K users we may start by defining a
correlation matrix C of size KxK and then for the square root of this matrix as C = MM T and then obtain
the samples as
where = [ ξc
( x
1, y1) , K,
ξc
( xK
, yK
)]
with mean zero and variance one. Alternatively, ( x y)
G = Mn , (6.5)
G and n is Kx1 vector of independent real-valued Gaussian variables
?
c
, can be generated for a grid of points by first
generating a grid of independent samples and then apply an appropriate two-dimensional filter. <strong>Final</strong>ly,
interpolation is used to find the value for a specific x
i
, yi
. In this approach the resolution of the grid
should be much finer that the correlation distance λ c .
After having obtained ( x, y)
? the actual large-scale parameters are obtained as
( µ )
( ) = − 1 0.5
x , y g R ( 0) ?( x,
y)
s +
, (6.6)
0.5
T 0.5
5
where R ( 0)
is obtained from the eigendecomposition R( 0) = EΛE
as R ( 0) = EΛ
0.
required parameters are found in Sectrion 3.
, and the
Page 137 (167)