"WINNER II Channel Models", ver 1.1, Sept

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