CNN‐based CSI acquisition for FDD massive MIMO with noisy feedback

Abstract

In frequency-division-duplex (FDD) massive multiple-input multiple-output (MIMO) systems, noisy feedback is a constant challenge for the base station (BS) to acquire accurate downlink channel state information (CSI). In this Letter, the authors propose a convolutional neural network (CNN)-based approach to overcome this problem, which they refer to it as an anti-noise CSI acquisition network (ANCAN). Results demonstrate that ANCAN can reconstruct CSI more accurately than other emerging CSI acquisition methods in the presence of noisy feedback links.