Channel state information (CSI) feedback is necessary for the frequency
division duplexing (FDD) multiple input multiple output (MIMO) systems due to
the channel non-reciprocity. With the help of deep learning, many works have
succeeded in rebuilding the compressed ideal CSI for massive MIMO. However,
simple CSI reconstruction is of limited practicality since the channel
estimation and the targeted beamforming design are not considered. In this
paper, a jointly optimized network is introduced for channel estimation and
feedback so that a spectral-efficient beamformer can be learned. Moreover, the
deployment-friendly subarray hybrid beamforming architecture is applied and a
practical lightweight end-to-end network is specially designed. Experiments
show that the proposed network is over 10 times lighter at the
resource-sensitive user equipment compared with the previous state-of-the-art
method with only a minor performance loss.

本文介绍了一种联合优化的网络，用于信道估计和反馈，以学习光谱效率波束形成器。同时，应用了部署友好型子阵混合波束形成架构，并特别设计了实用的轻量级端到端网络。实验证明，与仅具有轻微性能损失的先前现有技术方法相比，所提出的网络在资源敏感的用户设备上更轻了 10 倍以上。