  Learning-based techniques such as artificial intelligence (AI) and machine
learning (ML) play an increasingly important role in the development of future
communication networks. The success of a learning algorithm depends on the
quality and quantity of the available training data. In the physical layer
(PHY), channel information data can be obtained either through measurement
campaigns or through simulations based on predefined channel models. Performing
measurements can be time consuming while only gaining information about one
specific position or scenario. Simulated data, on the other hand, are more
generalized and reflect in most cases not a real environment but instead, a
statistical approximation based on a mathematical model. This paper presents a
procedure for acquiring channel data by means of fast and flexible software
defined radio (SDR) based channel measurements along with a method for a
parameter extraction that provides configuration input to the simulator. The
procedure from the measurement to the simulated channel data is demonstrated in
two exemplary propagation scenarios. It is shown, that in both cases the
simulated data is in good accordance to the measurements
