Binary Neural Network Aided CSI Feedback in Massive MIMO System

In massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station (BS) to achieve high performance gain. The CSI matrix needs be estimated and fed back from user equipment (UE) in frequency division duplexing (FDD) mode. Recently, deep learning widely used compression reduce feedback overhead. However, applying neural network brings extra memory computation cost, which non-negligible especially resource limited UE. this letter, a novel binarization aided named BCsiNet introduced lighten encoder at proposed offers over 30× saving around 2× inference acceleration compared with CsiNet. Moreover, experiments show that of can comparable original CsiNet even binarized.