mlsanityData to 14 Oct 2026

Digital Twin Enabled Site Specific Channel Precoding: Over the Air CIR Inference

arXiv 2501.16504 ↗First posted 27 Jan 2025eess.SP cs.AIcited by 0

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Abstract (arXiv)

This paper investigates the significance of designing a reliable, intelligent, and true physical environment-aware precoding scheme by leveraging an accurately designed channel twin model to obtain realistic channel state information (CSI) for cellular communication systems. Specifically, we propose a fine-tuned multi-step channel twin design process that can render CSI very close to the CSI of the actual environment. After generating a precise CSI, we execute precoding using the obtained CSI at the transmitter end. We demonstrate a two-step parameters' tuning approach to design channel twin by ray tracing (RT) emulation, then further fine-tuning of CSI by employing an artificial intelligence (AI) based algorithm can significantly reduce the gap between actual CSI and the fine-tuned digital twin (DT) rendered CSI. The simulation results show the effectiveness of the proposed novel approach in designing a true physical environment-aware channel twin model.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 16 references that are in the map. First posted on arXiv 27 Jan 2025.

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References · 7

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018Deep Learning-Based Channel Estimationcited by 75
  3. 2023Sionna RT: Differentiable Ray Tracing for Radio Propagation Modelingcited by 62
  4. 2023Real-Time Digital Twins: Vision and Research Directions for 6G and Beyondcited by 26
  5. 2023Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimatescited by 14
  6. 2020Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systemscited by 9
  7. 2024Digital Twin Aided Massive MIMO: CSI Compression and Feedbackcited by 3

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