  In massive multiple-input multiple-output (MIMO) systems, the user equipment
(UE) needs to feed the channel state information (CSI) back to the base station
(BS) for the following beamforming. But the large scale of antennas in massive
MIMO systems causes huge feedback overhead. Deep learning (DL) based methods
can compress the CSI at the UE and recover it at the BS, which reduces the
feedback cost significantly. But the compressed CSI must be quantized into bit
streams for transmission. In this paper, we propose an adaptor-assisted
quantization strategy for bit-level DL-based CSI feedback. First, we design a
network-aided adaptor and an advanced training scheme to adaptively improve the
quantization and reconstruction accuracy. Moreover, for easy practical
employment, we introduce the expert knowledge of data distribution and propose
a pluggable and cost-free adaptor scheme. Experiments show that compared with
the state-of-the-art feedback quantization method, this adaptor-aided
quantization strategy can achieve better quantization accuracy and
reconstruction performance with less or no additional cost. The open-source
codes are available at https://github.com/zhang-xd18/QCRNet.
