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