  The design of precoding plays a crucial role in achieving a high downlink
sum-rate in multiuser multiple-input multiple-output (MIMO) orthogonal
frequency-division multiplexing (OFDM) systems. In this correspondence, we
propose a deep learning based joint CSI feedback and multiuser precoding method
in frequency division duplex systems, aiming at maximizing the downlink
sum-rate performance in an end-to-end manner. Specifically, the eigenvectors of
the CSI matrix are compressed using deep joint source-channel coding
techniques. This compression method enhances the resilience of the feedback CSI
information against degradation in the feedback channel. A joint multiuser
precoding module and a power allocation module are designed to adjust the
precoding direction and the precoding power for users based on the feedback CSI
information. Experimental results demonstrate that the downlink sum-rate can be
significantly improved by using the proposed method, especially in scenarios
with low signal-to-noise ratio and low feedback overhead.
