  In modern wireless network architectures, such as O-RAN, artificial
intelligence (AI)-based applications are deployed at intelligent controllers to
carry out functionalities like scheduling or power control. The AI "apps" are
selected on the basis of contextual information such as network conditions,
topology, traffic statistics, and design goals. The mapping between context and
AI model parameters is ideally done in a zero-shot fashion via an automatic
model selection (AMS) mapping that leverages only contextual information
without requiring any current data. This paper introduces a general methodology
for the online optimization of AMS mappings. Optimizing an AMS mapping is
challenging, as it requires exposure to data collected from many different
contexts. Therefore, if carried out online, this initial optimization phase
would be extremely time consuming. A possible solution is to leverage a digital
twin of the physical system to generate synthetic data from multiple simulated
contexts. However, given that the simulator at the digital twin is imperfect, a
direct use of simulated data for the optimization of the AMS mapping would
yield poor performance when tested in the real system. This paper proposes a
novel method for the online optimization of AMS mapping that corrects for the
bias of the simulator by means of limited real data collected from the physical
system. Experimental results for a graph neural network-based power control app
demonstrate the significant advantages of the proposed approach.
