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WINNER II pdf - Final Report - Cept

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<strong>WINNER</strong> <strong>II</strong> D1.1.2 V1.2<br />

Let us assume we have M LSPs per link and K correlated links, i.e. K MSs linked to the same BS site at<br />

locations (x k ,y k ), where k = 1,…,K. Auto-correlation is generated to the LSPs the following way. At first<br />

we generate a uniform grid of locations based on co-ordinates of the K MSs. Size of the grid is<br />

( max( xk ) − min( xk<br />

) + 2D) x ( max( yk<br />

) − min( yk<br />

) + 2D)<br />

. To each grid node we assign M Gaussian iid<br />

~N(0,1) random numbers, one for each LSP. Then the grid of random numbers is filtered with a two<br />

dimensional FIR filter to generate exponential auto-correlation. Impulse response of the filter for the mth<br />

LSP is<br />

⎛ d<br />

( ) ⎟ ⎞<br />

h ⎜<br />

m<br />

d = exp − , (3.12)<br />

⎝ ∆<br />

m ⎠<br />

where d is distance and ∆ m is the correlation distance both in meters (see Table 4-5). Each of the M<br />

random numbers in nodes of the grid, representing M LSPs, is filtered with a specific filter, because the<br />

correlation distances may be different in Table 4-5. After filtering the correlated random numbers<br />

ξ x , y ) at K grid nodes (K MS locations) are saved and the redundant grid nodes are discarded.<br />

M<br />

(<br />

k k<br />

Cross-correlation is generated independently to the LSPs of K links by linear transformation<br />

where elements of correlation matrix<br />

are defined in Table 4-5.<br />

~<br />

s ( x , y ) = C (0) ξ ( x , y ) , (3.13)<br />

k<br />

k<br />

MxM<br />

M<br />

k<br />

⎡ C~ ⎤<br />

s ~ (0)<br />

~ ~ (0)<br />

1s<br />

L C<br />

1<br />

s1sM<br />

⎢<br />

⎥<br />

C<br />

MxM<br />

(0) = ⎢ M O M ⎥<br />

(3.14)<br />

⎢ ~ ~<br />

⎥<br />

⎣<br />

Cs<br />

(0)<br />

~ ~ (0)<br />

M s<br />

L C<br />

1<br />

sM<br />

sM<br />

⎦<br />

k<br />

3.4 Concept of channel segments, drops and time evolution<br />

Channel segment represents a period of quasi-stationarity during which probability distributions of lowlevel<br />

parameters are not changed noticeably. During this period all large-scale parameters, as well as<br />

velocity and direction-of-travel for mobile station (MS), are practically constant. To be physically<br />

feasible, the channel segment must be relatively confined in distance. The size depends on the<br />

environment, but it can be at maximum few meters. Correlation distances of different parameters describe<br />

roughly the proper size of the channel segment, see the paragraph 4.4.<br />

Allowing the channel segment length go to zero, we specify a drop: In a drop all parameters are fixed,<br />

except the phases of the rays. Motion within a drop is only virtual and causes fast fading and the Doppler<br />

effect by superposition of rotating phasors, rays. It can be said, that a drop is an abstract representation of<br />

a channel segment, where the inaccuracies caused by the change of the terminal location have been<br />

removed. In a simulation, the duration of a drop can be selected as desired. It is a common practice to use<br />

drops in the simulations. The main advantage is the simplicity of the simulation, because successive<br />

simulation runs do not need to be correlated. The drawback is that it is not possible to simulate cases,<br />

where variable channel conditions are needed. However, the drop-based simulation is the main method of<br />

simulations in <strong>WINNER</strong> projects I and <strong>II</strong>. In the final <strong>WINNER</strong> <strong>II</strong> Channel Models there is also an<br />

alternative for the drop-based simulation, i.e. simulation with time evolution., where correlated drops are<br />

used<br />

In the <strong>WINNER</strong> <strong>II</strong> models the propagation parameters may vary over time between the channel segments.<br />

In the multi segment modelling two options are available, either drops (stationary channel segments like<br />

in <strong>WINNER</strong> I) or continuous channel evolution with smooth transitions between segments. There are two<br />

approaches for time evolution modelling discussed below. First is the one that is proposed to be<br />

implemented, due to the simplicity of the method. Second is a method using Markov process that can be<br />

regarded as a more advanced method and it requires parameters that have not been determined yet.<br />

3.4.1 Basic method for time-evolution<br />

In this report time evolution of propagation parameters is modelled like depicted in Figure 3-8. The route<br />

to be modelled is covered by adjacent channel segments. The distance between segments is equal to the<br />

stationarity interval. Transition from segment to segment is carried out by replacing clusters of the “old”<br />

segment by the clusters of the “new” segment, one by one. The route between adjacent channel segments<br />

Page 33 (82)

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