Deep Learning Based Super-Resolution for Power Delay Profiles Towards Digital Twinning of Wireless Channels in 6G and Beyond

The digital twinning (DT) of wireless channels is crucial in the sixth-generation mobile communication system (6G) and beyond. Ray-tracing (RT) is a critical technology in DT of wireless channels with the limitation of significant computational resources. To address this challenge, this paper proposes a deep learning (DL) based super-resolution (SR) model for power delay profiles (PDP). This model can restore the PDP from low-resolution to high-resolution. The RT generates the datasets in outdoor and indoor scenarios at multi-frequency bands for training and test. Compared to the RT, the efficiency of this method is enhanced, and the accuracy is improved compared to the traditional linear interpolation method. In summary, the proposed method can serve as a reference for the DT of wireless channels.