  Some 6G use cases include augmented reality and high-fidelity holograms, with
this information flowing through the network. Hence, it is expected that 6G
systems can feed machine learning algorithms with such context information to
optimize communication performance. This paper focuses on the simulation of 6G
MIMO systems that rely on a 3-D representation of the environment as captured
by cameras and eventually other sensors. We present new and improved Raymobtime
datasets, which consist of paired MIMO channels and multimodal data. We also
discuss tradeoffs between speed and accuracy when generating channels via
ray-tracing. We finally provide results of beam selection and channel
estimation to assess the impact of the improvements in the ray-tracing
simulation methodology.
