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Wireless Digital Twin Calibration: Refining DFT-Domain Channel Information

Hao Luo, Saeed R. Khosravirad, Ahmed Alkhateeb

Abstract

Wireless digital twins can be leveraged to provide site-specific synthetic channel information through precise physical modeling and signal propagation simulations. This can help reduce the overhead of channel state information (CSI) acquisition, particularly needed for large-scale MIMO systems. For high-quality digital twin channels, the classical approach is to increase the digital twin fidelity via more accurate modeling of the environment, propagation, and hardware. This, however, comes with high computational cost, making it unsuitable for real-time applications. In this paper, we propose a new framework that, instead of calibrating the digital twin model itself, calibrates the DFT-domain channel information to reduce the gap between the low-fidelity digital twin and its high-fidelity counterpart or the real world. This allows systems to leverage a low-complexity digital twin for generating real-time channel information without compromising quality. To evaluate the effectiveness of the proposed approach, we adopt codebook-based CSI feedback as a case study, where refined synthetic channel information is used to identify the most relevant DFT codewords for each user. Simulation results demonstrate the effectiveness of the proposed digital twin calibration approach in achieving high CSI acquisition accuracy while reducing the computational overhead of the digital twin. This paves the way for realizing digital twin assisted wireless systems.

Wireless Digital Twin Calibration: Refining DFT-Domain Channel Information

Abstract

Wireless digital twins can be leveraged to provide site-specific synthetic channel information through precise physical modeling and signal propagation simulations. This can help reduce the overhead of channel state information (CSI) acquisition, particularly needed for large-scale MIMO systems. For high-quality digital twin channels, the classical approach is to increase the digital twin fidelity via more accurate modeling of the environment, propagation, and hardware. This, however, comes with high computational cost, making it unsuitable for real-time applications. In this paper, we propose a new framework that, instead of calibrating the digital twin model itself, calibrates the DFT-domain channel information to reduce the gap between the low-fidelity digital twin and its high-fidelity counterpart or the real world. This allows systems to leverage a low-complexity digital twin for generating real-time channel information without compromising quality. To evaluate the effectiveness of the proposed approach, we adopt codebook-based CSI feedback as a case study, where refined synthetic channel information is used to identify the most relevant DFT codewords for each user. Simulation results demonstrate the effectiveness of the proposed digital twin calibration approach in achieving high CSI acquisition accuracy while reducing the computational overhead of the digital twin. This paves the way for realizing digital twin assisted wireless systems.
Paper Structure (14 sections, 11 equations, 4 figures, 1 table)

This paper contains 14 sections, 11 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Signal and Channel Models
  4. Digital Twin Model
  5. Problem Formulation
  6. Proposed Solution
  7. Key Idea
  8. Training Approaches of the Calibration Model
  9. Deep Learning Model Architecture
  10. Case Study: Codebook-Based CSI Feedback
  11. Simulation Results
  12. Simulation Setup
  13. Performance Evaluation
  14. Conclusion

Figures (4)

  • Figure 1: This figure illustrates the proposed digital twin calibration framework and its use case of codebook-based CSI feedback. The BS generates synthetic channel information for a user using a low-fidelity digital twin. The synthetic channel information is then refined using a lightweight deep learning model. The refined channel information is used to select the most relevant DFT codewords for that user. The user receives pilot signals corresponding to these selected codewords and estimates their coefficients, which are reported back to the BS for downlink precoder design.
  • Figure 2: This figure shows the bird's-eye view of the Arizona State University (ASU) campus, which serves as the study area for the simulation. The BS is located at the rooftop of a building at the top right corner, and the user grid is highlighted by the red box.
  • Figure 3: This figure presents the heatmaps illustrating the top-4 DFT beam indices selected by the Sionna RT (baseline) and Wireless Insite (target) ray tracers. The key observation is that larger differences occur when the user is situated in the NLoS region (the upper and lower-left areas of the user grid), which is aligned with the fact that the baseline scenario employs a ray tracer with a simplified diffraction model.
  • Figure 4: This figure presents the CDF of the cosine similarity between the ground-truth channel and the estimated CSI using the top-4 DFT beams selected by the proposed calibration approach and the benchmark scenarios. By integrating a lightweight calibration model (inference: $0.0018$ s/sample), our approach nears the performance upper bound while maintaining a total cost of $0.0610$ s/sample, which is significantly lower than the $1.2019$ s/sample required by a high-fidelity digital twin.