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.

翻译：频道状态信息(CSI)反馈对于频道不对等的频率分解(DFD)多输入多重输出(MIMO)系统是必要的。在深层学习的帮助下,许多工程成功地重建了大型IMO的压缩理想 CSI。然而,简单的 CSI重建是有限的实用性,因为频道估计和定向波束设计没有考虑。本文采用了一个联合优化的频道估计和反馈网络,以便能够学习光谱高效光谱波束。此外,还应用了方便部署的亚阵列混合波束成型结构,并专门设计了一个实用的轻量端对端网络。实验显示,与以往最先进的方法相比,对资源敏感的用户设备的拟议网络较轻10倍以上,只有轻微的性能损失。