  In frequency division duplex (FDD) multiple-input multiple-output (MIMO)
wireless communications, limited channel state information (CSI) feedback is a
central tool to support advanced single- and multi-user MIMO
beamforming/precoding. To achieve a given CSI quality, the CSI quantization
codebook size has to grow exponentially with the number of antennas, leading to
quantization complexity, as well as, feedback overhead issues for larger MIMO
systems. We have recently proposed a multi-stage recursive Grassmannian
quantizer that enables a significant complexity reduction of CSI quantization.
In this paper, we show that this recursive quantizer can effectively be
combined with deep learning classification to further reduce the complexity,
and that it can exploit temporal channel correlations to reduce the CSI
feedback overhead.
