  Channel state information (CSI) feedback is necessary for the frequency
division duplexing (FDD) multiple input multiple output (MIMO) systems due to
the channel non-reciprocity. With the help of deep learning, many works have
succeeded in rebuilding the compressed ideal CSI for massive MIMO. However,
simple CSI reconstruction is of limited practicality since the channel
estimation and the targeted beamforming design are not considered. In this
paper, a jointly optimized network is introduced for channel estimation and
feedback so that a spectral-efficient beamformer can be learned. Moreover, the
deployment-friendly subarray hybrid beamforming architecture is applied and a
practical lightweight end-to-end network is specially designed. Experiments
show that the proposed network is over 10 times lighter at the
resource-sensitive user equipment compared with the previous state-of-the-art
method with only a minor performance loss.
