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Massive multiple-input multiple-output (MIMO) is a promising technology toincrease link capacity and energy efficiency. However, these benefits are basedon 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 BSfor massive MIMO seriously increase this overhead. In this paper, we propose amultiple-rate compressive sensing neural network framework to compress andquantize the CSI. This framework not only improves reconstruction accuracy butalso 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, anddevelop a novel quantization framework and training strategy. Next, we furtherintroduce two different variable-rate approaches, namely, SM-CsiNet+ andPM-CsiNet+, which decrease the parameter number at the UE by 38.0% and 46.7%,respectively. Experimental results show that CsiNet+ outperforms thestate-of-the-art network by a margin but only slightly increases the parameternumber. We also investigate the compression and reconstruction mechanism behinddeep learning-based CSI feedback methods via parameter visualization, whichprovides a guideline for subsequent research.

Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis