Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimates

• URL: http://arxiv.org/abs/2312.12625v2
• Date: Tue, 14 May 2024 18:34:52 GMT
• Title: Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimates
• Authors: Clement Ruah, Osvaldo Simeone, Jakob Hoydis, Bashir Al-Hashimi,
• Abstract summary: ray tracing (RT) is widely seen as an enabling technology for DTs of the radio access network (RAN) segment of next-generation wireless systems. The effectiveness of RT hinges on the adaptation of the electromagnetic properties assumed by the RT to actual channel conditions. This paper proposes a novel channel response-based scheme that estimates and compensates for the phase errors in the channel responses.
• Score: 40.04476706955071
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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