  Positioning benefits from channel models that capture geometric effects and,
in particular, from the signal properties of the first arriving path and the
spatial consistency of the propagation condition of multiple links. The models
that capture the physical effects observed in a realistic deployment scenario
are essential for assessing the potential benefits of enhancements in
positioning methods. Channel models based on ray-tracing simulations and
statistical channel models, which are current state-of-the-art methods employed
to evaluate performance of positioning in 3GPP systems, do not fully capture
important aspects applicable to positioning. Hence, we propose an extension of
existing statistical channel models with semi-deterministic clusters (SDCs).
SDCs allow channels to be simulated using three types of clusters: fixed-,
specular-, and random-clusters. Our results show that the proposed model aligns
with measurements obtained in a real deployment scenario. Thus, our channel
models can be used to develop advanced positioning solutions based on machine
learning, which enable positioning with centimeter level accuracy in NLOS and
multipath scenarios.
