  In frequency division duplex mode of massive multiple-input multiple-output
systems, the downlink channel state information (CSI) must be sent to the base
station (BS) through a feedback link. However, transmitting CSI to the BS is
costly due to the bandwidth limitation of the feedback link. Deep learning (DL)
has recently achieved remarkable success in CSI feedback. Realizing
high-performance and low-complexity CSI feedback is a challenge in DL based
communication. We develop a DL based CSI feedback network in this study to
complete the feedback of CSI effectively. However, this network cannot be
effectively applied to the mobile terminal because of the excessive numbers of
parameters. Therefore, we further propose a new lightweight CSI feedback
network based on the developed network. Simulation results show that the
proposed CSI network exhibits better reconstruction performance than that of
other CsiNet-related works. Moreover, the lightweight network maintains a few
parameters and parameter complexity while ensuring satisfactory reconstruction
performance. These findings suggest the feasibility and potential of the
proposed techniques.
