  In multiple-input multiple-output (MIMO) systems, the high-resolution channel
information (CSI) is required at the base station (BS) to ensure optimal
performance, especially in the case of multi-user MIMO (MU-MIMO) systems. In
the absence of channel reciprocity in frequency division duplex (FDD) systems,
the user needs to send the CSI to the BS. Often the large overhead associated
with this CSI feedback in FDD systems becomes the bottleneck in improving the
system performance. In this paper, we propose an AI-based CSI feedback based on
an auto-encoder architecture that encodes the CSI at UE into a low-dimensional
latent space and decodes it back at the BS by effectively reducing the feedback
overhead while minimizing the loss during recovery. Our simulation results show
that the AI-based proposed architecture outperforms the state-of-the-art
high-resolution linear combination codebook using the DFT basis adopted in the
5G New Radio (NR) system.
