  In frequency division duplexing (FDD) mode, it is necessary to send the
channel state information (CSI) from user equipment to base station. The
downlink CSI is essential for the massive multiple-input multiple-output (MIMO)
system to acquire the potential gain. Recently, deep learning is widely adopted
to massive MIMO CSI feedback task and proved to be effective compared with
traditional compressed sensing methods. In this paper, a novel network named
ACRNet is designed to boost the feedback performance with network aggregation
and parametric RuLU activation. Moreover, valid approach to expand the network
architecture in exchange of better performance is first discussed in CSI
feedback task. Experiments show that ACRNet outperforms loads of previous
state-of-the-art feedback networks without any extra information.
