Deep learning channel estimation algorithm for ultra-massive terahertz systems

In order to further improve the hybrid-field channel estimation performance in terahertz ultra-massive multiple-input multiple-output systems, an efficient cross channel Transformer module for image restoration and a fast Fourier transform convolutional network were introduced based on the fixed point network, and a scalable and efficient deep learning model FPN-OTFN was proposed, which models the channel estimation problem as an image restoration problem. Firstly, the least squares algorithm was used to obtain the channel information at the pilot location, and then the channel information was input into the proposed FPN-OTFN algorithm. By training and learning the mapping relationship between low precision channel images and high-precision images, the true channel state information was restored. The simulation results show that the proposed scheme not only inherits the high efficiency and adaptivity of the FPN framework, but also possesses high estimation accuracy and good robustness for THz channels.