  Channel state information (CSI) reporting is important for multiple-input
multiple-output (MIMO) transmitters to achieve high capacity and energy
efficiency in frequency division duplex (FDD) mode. CSI reporting for massive
MIMO systems could consume excessive bandwidth and degrade spectrum efficiency.
Deep learning (DL)-based compression integrated with channel correlations have
demonstrated success in improving CSI recovery. However, existing works
focusing on CSI compression have shown little on the efficient encoding of CSI
report. In this paper, we propose an efficient DL-based compression framework
(called CQNet) to jointly tackle CSI compression, report encoding, and recovery
under bandwidth constraint. CQNet can be directly integrated within other
DL-based CSI feedback works for further enhancement. CQNet significantly
outperforms solutions using uniform CSI quantization and $\mu$-law non-uniform
quantization. Compared with traditional CSI reporting, much fewer bits are
required to achieve comparable CSI reconstruction accuracy.
