  Embodying the principle of simulation intelligence, digital twin (DT) systems
construct and maintain a high-fidelity virtual model of a physical system. This
paper focuses on ray tracing (RT), which is widely seen as an enabling
technology for DTs of the radio access network (RAN) segment of next-generation
disaggregated wireless systems. RT makes it possible to simulate channel
conditions, enabling data augmentation and prediction-based transmission.
However, the effectiveness of RT hinges on the adaptation of the
electromagnetic properties assumed by the RT to actual channel conditions, a
process known as calibration. The main challenge of RT calibration is the fact
that small discrepancies in the geometric model fed to the RT software hinder
the accuracy of the predicted phases of the simulated propagation paths.
Existing solutions to this problem either rely on the channel power profile,
hence disregarding phase information, or they operate on the channel responses
by assuming the simulated phases to be sufficiently accurate for calibration.
This paper proposes a novel channel response-based scheme that, unlike the
state of the art, estimates and compensates for the phase errors in the
RT-generated channel responses. The proposed approach builds on the variational
expectation maximization algorithm with a flexible choice of the prior
phase-error distribution that bridges between a deterministic model with no
phase errors and a stochastic model with uniform phase errors. The algorithm is
computationally efficient, and is demonstrated, by leveraging the open-source
differentiable RT software available within the Sionna library, to outperform
existing methods in terms of the accuracy of RT predictions.
