  The use of deep learning (DL) for channel state information (CSI) feedback
has garnered widespread attention across academia and industry. The mainstream
DL architectures, e.g., CsiNet, deploy DL models on the base station (BS) side
and the user equipment (UE) side, which are highly coupled and need to be
trained jointly. However, two-sided DL models require collaborations between
different network vendors and UE vendors, which entails considerable challenges
in order to achieve consensus, e.g., model maintenance and responsibility.
Furthermore, DL-based CSI feedback design invokes DL to reduce only the CSI
feedback error, whereas jointly optimizing several modules at the transceivers
would provide more significant gains. This article presents DL-based CSI
feedback from the perspectives of one-sided model and joint multi-module
learning. We herein introduce various novel one-sided CSI feedback
architectures. In particular, the recently proposed CSI-PPPNet provides a
one-sided one-for-all framework, which allows a DL model to deal with arbitrary
CSI compression ratios. We review different joint multi-module learning
methods, where the CSI feedback module is learned jointly with other modules
including channel coding, channel estimation, pilot design and precoding
design. Finally, future directions and challenges for DL-based CSI feedback are
discussed, from the perspectives of inherent limitations of artificial
intelligence (AI) and practical deployment issues.
