  The increased throughput brought by MIMO technology relies on the knowledge
of channel state information (CSI) acquired in the base station (BS). To make
the CSI feedback overhead affordable for the evolution of MIMO technology
(e.g., massive MIMO and ultra-massive MIMO), deep learning (DL) is introduced
to deal with the CSI compression task. Based on the separation principle in
existing communication systems, DL based CSI compression is used as source
coding. However, this separate source-channel coding (SSCC) scheme is inferior
to the joint source-channel coding (JSCC) scheme in the finite blocklength
regime. In this paper, we propose a deep joint source-channel coding (DJSCC)
based framework for the CSI feedback task. In particular, the proposed method
can simultaneously learn from the CSI source and the wireless channel. Instead
of truncating CSI via Fourier transform in the delay domain in existing
methods, we apply non-linear transform networks to compress the CSI.
Furthermore, we adopt an SNR adaption mechanism to deal with the wireless
channel variations. The extensive experiments demonstrate the validity,
adaptability, and generality of the proposed framework.
