  Accurate channel state information (CSI) feedback plays a vital role in
improving the performance gain of massive multiple-input multiple-output
(m-MIMO) systems, where the dilemma is excessive CSI overhead versus limited
feedback bandwith. By considering the noisy CSI due to imperfect channel
estimation, we propose a novel deep neural network architecture, namely
AnciNet, to conduct the CSI feedback with limited bandwidth. AnciNet extracts
noise-free features from the noisy CSI samples to achieve effective CSI
compression for the feedback. Experimental results verify that the proposed
AnciNet approach outperforms the existing techniques under various conditions.
