  Massive multiple-input multiple-output (MIMO) is a promising technology to
increase link capacity and energy efficiency. However, these benefits are based
on available channel state information (CSI) at the base station (BS).
Therefore, user equipment (UE) needs to keep on feeding CSI back to the BS,
thereby consuming precious bandwidth resource. Large-scale antennas at the BS
for massive MIMO seriously increase this overhead. In this paper, we propose a
multiple-rate compressive sensing neural network framework to compress and
quantize the CSI. This framework not only improves reconstruction accuracy but
also decreases storage space at the UE, thus enhancing the system feasibility.
Specifically, we establish two network design principles for CSI feedback,
propose a new network architecture, CsiNet+, according to these principles, and
develop a novel quantization framework and training strategy. Next, we further
introduce two different variable-rate approaches, namely, SM-CsiNet+ and
PM-CsiNet+, which decrease the parameter number at the UE by 38.0% and 46.7%,
respectively. Experimental results show that CsiNet+ outperforms the
state-of-the-art network by a margin but only slightly increases the parameter
number. We also investigate the compression and reconstruction mechanism behind
deep learning-based CSI feedback methods via parameter visualization, which
provides a guideline for subsequent research.
