  In 3GPP New Radio (NR) Release 18 we see the first study item starting in May
2022, which will evaluate the potential of AI/ML methods for Radio Access
Network (RAN) 1, i.e., for mobile radio PHY and MAC layer applications. We use
the profiling method for accurate iterative estimation of multipath component
parameters for PHY layer reference, as it promises a large channel prediction
horizon. We investigate options to partly or fully replace some functionalities
of this rule based PHY layer method by AI/ML inferences, with the goal to
achieve either a higher performance, lower latency, or, reduced processing
complexity. We provide first results for noise reduction, then a combined
scheme for model order selection, compare options to infer multipath component
start parameters, and, provide an outlook on a possible channel prediction
framework.

Signal Processing;Machine Learning