  Quantized channel state information (CSI) plays a critical role in precoding
design which helps reap the merits of multiple-input multiple-output (MIMO)
technology. In order to reduce the overhead of CSI feedback, we propose a deep
learning based CSI quantization method by developing a joint convolutional
residual network (JCResNet) which benefits MIMO channel feature extraction and
recovery from the perspective of bit-level quantization performance.
Experiments show that our proposed method substantially improves the
performance.
