  In frequency division duplex mode, the downlink channel state information
(CSI) should be sent to the base station through feedback links so that the
potential gains of a massive multiple-input multiple-output can be exhibited.
However, such a transmission is hindered by excessive feedback overhead. In
this letter, we use deep learning technology to develop CsiNet, a novel CSI
sensing and recovery {mechanism} that learns to effectively use channel
structure from training samples. CsiNet learns a transformation from CSI to a
near-optimal number of representations (or codewords) and an inverse
transformation from codewords to CSI. We perform experiments to demonstrate
that CsiNet can recover CSI with significantly improved reconstruction quality
compared with existing compressive sensing (CS)-based methods. Even at
excessively low compression regions where CS-based methods cannot work, CsiNet
retains effective beamforming gain.
