WINNER II pdf - Final Report - Cept
WINNER II pdf - Final Report - Cept
WINNER II pdf - Final Report - Cept
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<strong>WINNER</strong> <strong>II</strong> D1.1.2 V1.2<br />
coefficients for whole the channel segment. In addition it is possible to define an extra attenuation or<br />
cases, where a moving object (e.g a person) is shadowing paths from other scatterers. However, we<br />
neglect this phenomenon for simplicity. The reasoning is as follows: The shadowing situation in the<br />
indoor environment is assumed to be statistically the same, irrespective of the position of the scatterers.<br />
Therefore we conclude that the measurements and literature results already contain this shadowed<br />
situation, precisely enough for our modelling needs.<br />
In indoors the moving objects (called clusters) are assumed to be humans. Reflection is the main<br />
interaction with human body at <strong>WINNER</strong> target frequency range, as analysed in [VES00] and [GTD+04].<br />
In our model only a cluster can be in linear motion for longer times, and this is modelled by an<br />
accompanying mean cluster Doppler shift. A cluster is composed of 20 rays. If the scatterer described by<br />
the cluster is assumed rigid, the relative movements come from the geometry and the movement of the<br />
cluster, and can be directly calculated from the geometric model plus the known motion. In addition, there<br />
are moving scatterers within a cluster (e.g. limbs), the parts of which are moving relatively. This<br />
phenomenon can be governed e.g. through a Doppler spectrum assigned to a cluster.<br />
Assumptions:<br />
1. A cluster can be either moving or static.<br />
2. A moving cluster has a random velocity that can be zero.<br />
3. Static cluster, contains no movement at all, moving cluster can have a random fluctuation on top<br />
of its mean movement (random velocity).<br />
4. A moving cluster can shadow signals from other clusters. (Neglected here, as discussed afore.)<br />
To create a model for the situation described afore, we have to fix the probabilities of static and moving<br />
clusters and the accompanying distributions of the directions of the rays and the Doppler spectra of the<br />
moving rays. The distributions for the directions of the rays, power levels etc. are all given by the<br />
ordinary random process (i.e. non-nomadic) for the creating of the channel coefficients. All that remains<br />
are the Doppler frequencies of the rays based on the virtual movement of the clusters. This means that, in<br />
addition to the ordinary process, we have to specify:<br />
- the number of static clusters (e.g. 80% of all clusters),<br />
- mean velocity and direction for all moving clusters, with some velocities being possibly<br />
zero (e.g. 50% zero velocity, 50% 3km/h, direction ~Uni(360°) (uniformly distributed<br />
over 360°)),<br />
- additional Doppler frequency for each of the moving scatterers (e.g. calculated by ray<br />
AoA/AoD, velocity 3km/h, direction of motion ~Uni(360°)),<br />
The number of moving scatterer in a cluster is determined by targeted cluster-wise temporal K-factor. The<br />
temporal K-factor will be K t = F/S, where F is the number of fixed rays and S is the total number of rays<br />
per cluster.<br />
3.6 Reduced complexity models<br />
A need has been identified for reduced-complexity channel models that can be used in rapid simulations<br />
having the objective of making comparisons between systems alternatives at link-level (e.g. modulation<br />
and coding choices). In this report, such models are referred to as reduced-complexity models, and have<br />
the character of the well-known tapped delay line class of fading channel models. However, to address<br />
the needs of MIMO channel modelling, temporal variations at the taps are determined by more detailed<br />
information than that required for the specification of relative powers, envelope fading distributions, and<br />
fading rates, which are typical inputs to traditional tapped delay line models.<br />
Specifically, multipath AoD and AoA information is inherent in the determination of tap fading<br />
characteristics. For these reasons, the reduced complexity models reported herein are referred to as<br />
Cluster Delay Line (CDL) models. A cluster is centred at each tap. In general, each cluster is comprised<br />
of the vector sum of equal-powered MPCs (sinusoids), all of which have the same or close to same delay.<br />
Each MPC has a varying phase, but has fixed AoA and AoD offsets. The latter depend on the angular<br />
spreads at the MS and the BS, respectively, as shown in Table 4-1. The values in this table were chosen to<br />
realise a specified Laplacian PAS for each cluster, appropriate to the scenario being modelled. In cases<br />
where there is a desire to simulate Ricean-like fading, an extra MPC is added, which is given a power<br />
appropriate to the desired Rice factor, and zero angular offset. The powers and delays of the clusters can<br />
be non-uniform, and can be chosen to realise the desired overall channel rms delay spread. Parameters of<br />
all CDL models reflect the expected values of those used in the more complex models described in other<br />
sections of this report.<br />
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