  The Massive Multiple Input Multiple Output (MIMO) system is a core technology
of the next generation communication. With the growing complexity of CSI in
massive MIMO system, traditional compressive sensing based CSI feedback has
become a bottleneck problem that is limited in piratical. Recently, numerous
deep learning based CSI feedback approaches demonstrate the efficiency and
potential. However, the existing methods lack a reasonable interpretation of
the deep learning model and the accuracy of the model decreases significantly
as the CSI compression rate increases.
  In this paper, from the intrinsic properties of CSI data itself, we devised
the corresponding deep learning building blocks to compose a novel neural
network CQNet and experiment result shows CQNet outperform the state-of-the-art
method with less computational overhead by achieving an average performance
improvement of 8.07% in both outdoor and indoor scenarios. In addition, this
paper also investigates the reasons for the decrease in model accuracy at large
compression rates and proposes a strategy to embed a quantization layer to
achieve effective compression, by which the original accuracy loss of 67.19% on
average is reduced to 21.96% on average, and the compression rate is increased
by 8 times on the original benchmark.
