  Channel state information (CSI) feedback is critical for frequency division
duplex (FDD) massive multi-input multi-output (MIMO) systems. Most conventional
algorithms are based on compressive sensing (CS) and are highly dependent on
the level of channel sparsity. To address the issue, a recent approach adopts
deep learning (DL) to compress CSI into a codeword with low dimensionality,
which has shown much better performance than the CS algorithms when feedback
link is perfect. In practical scenario, however, there exists various
interference and non-linear effect. In this article, we design a DL-based
denoise network, called DNNet, to improve the performance of channel feedback.
Numerical results show that the DL-based feedback algorithm with the proposed
DNNet has superior performance over the existing algorithms, especially at low
signal-to-noise ratio (SNR).
