# Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimates arXiv: https://arxiv.org/abs/2312.12625 ยท HTML: https://mlsanity.com/paper/2312.12625/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 58 references that are in the map. - First posted on arXiv 19 Dec 2023. ## Builds on (most cited first, 21 of 21) - [Neuralangelo: High-Fidelity Neural Surface Reconstruction](https://mlsanity.com/paper/2306.03092/) (2023) - [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022) - [Simulation Intelligence: Towards a New Generation of Scientific Methods](https://mlsanity.com/paper/2112.03235/) (2021) - [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023) - [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022) - [Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions](https://mlsanity.com/paper/2102.12169/) (2021) - [A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies](https://mlsanity.com/paper/2208.14197/) (2022) - [Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond](https://mlsanity.com/paper/2301.11283/) (2023) - [6G Digital Twin Networks: From Theory to Practice](https://mlsanity.com/paper/2212.02032/) (2022) - [Learning Radio Environments by Differentiable Ray Tracing](https://mlsanity.com/paper/2311.18558/) (2023) - [A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems](https://mlsanity.com/paper/2212.01351/) (2022) - [Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2110.01848/) (2021) - [Digital Twin Based Beam Prediction: Can we Train in the Digital World and Deploy in Reality?](https://mlsanity.com/paper/2301.07682/) (2023) - [Digital Twin for Networking: A Data-driven Performance Modeling Perspective](https://mlsanity.com/paper/2206.00310/) (2022) - [Wireless Channel Charting: Theory, Practice, and Applications](https://mlsanity.com/paper/2304.08095/) (2023) - [Colosseum as a Digital Twin: Bridging Real-World Experimentation and Wireless Network Emulation](https://mlsanity.com/paper/2303.17063/) (2023) - [Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming](https://mlsanity.com/paper/2305.10350/) (2023) - [Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks: Research Directions for Security and Optimal Control](https://mlsanity.com/paper/2204.01950/) (2022) - [Tuning of Ray-Based Channel Model for 5G Indoor Industrial Scenarios](https://mlsanity.com/paper/2309.06101/) (2023) - [Communication Through a Large Reflecting Surface With Phase Errors](https://mlsanity.com/paper/1906.10751/) (2019) - [Probabilistic Ray-Tracing Aided Positioning at mmWave frequencies](https://mlsanity.com/paper/2308.08441/) (2023) ## Built on by (most cited first, 14 of 14) - [How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks](https://mlsanity.com/paper/2507.07067/) (2025) - [Spatial Channel State Information Prediction with Generative AI: Towards Holographic Communication and Digital Radio Twin](https://mlsanity.com/paper/2401.08023/) (2024) - [Multi-Fidelity Bayesian Optimization With Across-Task Transferable Max-Value Entropy Search](https://mlsanity.com/paper/2403.09570/) (2024) - [Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning](https://mlsanity.com/paper/2406.15819/) (2024) - [Context-Aware Doubly-Robust Semi-Supervised Learning](https://mlsanity.com/paper/2502.15577/) (2025) - [Wideband RF Radiance Field Modeling Using Frequency-embedded 3D Gaussian Splatting](https://mlsanity.com/paper/2505.20714/) (2025) - [Digital Twin Enabled Site Specific Channel Precoding: Over the Air CIR Inference](https://mlsanity.com/paper/2501.16504/) (2025) - [Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition](https://mlsanity.com/paper/2505.01783/) (2025) - [Digital Twin-Assisted Measurement Design and Channel Statistics Prediction](https://mlsanity.com/paper/2603.23787/) (2026) - [mmRadarTwin: A Measurement-Calibrated Signal-Level Digital Twin Platform for Indoor mmWave Radar](https://mlsanity.com/paper/2607.28108/) (2026) - [Should I Use This Synthetic Dataset for Training? How to Test with Minimal Real Data](https://mlsanity.com/paper/2608.27996/) (2026) - [Tri-Band Channel Measurement-Enabled Multi-Layer Digital Twin for Terahertz Wireless Data Centers](https://mlsanity.com/paper/2609.01699/) (2026) - [TRACE: Learning to Self-Calibrate Wireless Digital Twins from ISAC Measurements](https://mlsanity.com/paper/2609.32923/) (2026) - [Geometry-Aided Channel Deduction with Partial Channel Estimates and Uncalibrated Digital Twin](https://mlsanity.com/paper/2609.37277/) (2026) ## Abstract (arXiv) 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.