Deep learning (DL) approaches have demonstrated high performance in compressing and reconstructing the channel state information (CSI) and reducing the CSI feedback overhead in massive MIMO systems. One key challenge, however, with the DL approaches is the demand for extensive training data. Collecting this real-world CSI data incurs significant overhead that hinders the DL approaches from scaling to a large number of communication sites. To address this challenge, we propose a novel direction that utilizes site-specific \textit{digital twins} to aid the training of DL models. The proposed digital twin approach generates site-specific synthetic CSI data from the EM 3D model and ray tracing, which can then be used to train the DL model without real-world data collection. To further improve the performance, we adopt online data selection to refine the DL model training with a small real-world CSI dataset. Results show that a DL model trained solely on the digital twin data can achieve high performance when tested in a real-world deployment. Further, leveraging domain adaptation techniques, the proposed approach requires orders of magnitude less real-world data to approach the same performance of the model trained completely on a real-world CSI dataset.

翻译：深度学习（DL）方法在压缩和重建信道状态信息（CSI）以及降低大规模MIMO系统中CSI反馈开销方面展现出高性能。然而，深度学习方法的一个关键挑战是对大量训练数据的需求。采集真实世界的CSI数据会产生巨大开销，阻碍了深度学习方法扩展至大量通信站点。为解决此问题，我们提出利用站点特定的数字孪生辅助深度学习模型训练的新方向。所提出的数字孪生方法通过电磁三维模型和射线追踪生成站点特定的合成CSI数据，可在无需真实数据采集的情况下训练深度学习模型。为进一步提升性能，我们采用在线数据选择，利用少量真实CSI数据集优化深度学习模型训练。结果表明，仅基于数字孪生数据训练的深度学习模型在真实部署环境中测试时仍能实现高性能。此外，借助域适应技术，所提方法仅需数量级更少的真实数据即可达到完全基于真实CSI数据集训练模型的同等性能。