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

WINNER D5.4 v. 1.4
4. Modelling Approaches
In this section, we discuss the modelling and coefficient generation approach of existing spatial channel
models. Then our selected approach is presented in detail.
Apart from the generic, fully random channel model, we define a clustered delay-line model derived from
our generic model by limiting the randomness (fixing the value) of certain parameters. The reduced
variability aids the comparability of results based on shorter simulation times.
4.1 Generic channel modelling approach
The generic channel modelling approach has been followed earlier in COST259 and in 3GPP
standardization. In COST259, the approach has been followed for directional antenna channel models for
smart antennas wireless applications. The COST259 was mainly for antenna array application at one end,
usually the base station side. The 3GPP standardization channel model, known as the 3GPP/3GPP2
spatial channel model (SCM), was developed for MIMO approaches in third generation cellular systems.
A generic channel modelling approach can be thought as a channel model framework that can be applied
in different scenarios. Each scenario has scenario specific distributions and parameters. By changing the
scenario specific distributions in angle and delay domains as well as the scenario specific parameters, we
can have different channel models for different scenarios under the same framework of the channel
model.
4.1.1 Distinction between channel models for link-level and system-level simulation
Workpackage 5, as defined in the Annex, is divided into a link-level and a system-level modelling effort
with task 4 representing the former and task 5 the latter part. During the evolution of our work though, we
found that we had to be very careful with such a division because it turned out not to be inherently clear
where to draw the line. To counter this problem, we consequently defined a set of properties for each of
the two levels in our deliverable D5.2. It has turned out most practical to implement both the link-level
and system-level features in one model. Here we understand the system-level channel modelling as in the
SCM model [3GPP SCM]. Then it is possible to emphasize either the system-level features or the linklevel
features or both by selecting the parameters properly.
Our conclusion is that it can be potentially dangerous to define a certain division and separate the two
channel models. Consider for example a model where shadowing is considered a higher level than delayor
angle-spread and for this reason treated independently. As a consequence, a likely conclusion drawn
from low-level simulations is that angle- and delay-spread significantly improves capacity. However, if
all three parameters were simulated corporately, including their cross-correlations, the solution might be
completely opposite, specifically that the capacity loss from shadowing outweighs the gain from delayand
angle-spread.
In summary, we favour channel models that contain both the link-level and the system-level features
defined at the same time. Hence, it depends on the application, which feature is switched on or off.
4.1.2 Comparison between deterministic and stochastic channel modeling
Channel modeling can be broadly split into two areas that differ in the goal or application and the type of
underlying data.
Deterministic channel modeling can be employed when detailed environment data is available. Detailed
environment data means position, size and orientation of man-made objects (houses, buildings, bridges,
roads, etc.) as well as natural objects (foliage or dominant plants, rocks, ground properties, etc.). The
basic idea is that if the propagation environment is known to a sufficient degree, wireless propagation is a
deterministic process that allows determining or predicting its characteristics at every point in space. It is
also referred to as propagation prediction and is the type of modeling used for cell planning, i.e., the
analysis of optimum locations for BS deployment and the prediction of the resulting coverage, capacity,
and data rates. In deterministic channel modeling, channel measurements are made in the same
environment for which detailed data is available and then used to optimize the match between prediction
model and measurements.
Stochastic channel modeling on the other hand is based on a stochastic view of the wireless channel.
Measurements are made in a large variety of locations and environments to obtain a data set with a good
representation of the underlying statistical properties. Influence parameters based on the environment
characteristics may be used to refine the statistical accuracy for similar environments. As such,
classification is an important tool to trade off accuracy versus universality of statements.
What we aim for in WINNER is the prediction of statistical behavior of the channel. Knowledge of
statistical channel parameters allows making more general statements. Especially, they allow evaluating
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