  Recently, inspired by successful applications in many fields, deep learning
(DL) technologies for CSI acquisition have received considerable research
interest from both academia and industry. Considering the practical feedback
mechanism of 5th generation (5G) New radio (NR) networks, we propose two
implementation schemes for artificial intelligence for CSI (AI4CSI), the
DL-based receiver and end-to-end design, respectively. The proposed AI4CSI
schemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE),
feedback overhead, and computational complexity, and compared with legacy
schemes. To demonstrate whether these schemes can be used in real-life
scenarios, both the modeled-based channel data and practically measured
channels were used in our investigations. When DL-based CSI acquisition is
applied to the receiver only, which has little air interface impact, it
provides approximately 25\% SE gain at a moderate feedback overhead level. It
is feasible to deploy it in current 5G networks during 5G evolutions. For the
end-to-end DL-based CSI enhancements, the evaluations also demonstrated their
additional performance gain on SE, which is 6% -- 26% compared with DL-based
receivers and 33% -- 58% compared with legacy CSI schemes. Considering its
large impact on air-interface design, it will be a candidate technology for 6th
generation (6G) networks, in which an air interface designed by artificial
intelligence can be used.
