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：信道状态信息（CSI）反馈对于频分双工（FDD）多输入多输出（MIMO）系统是必要的，因为信道不具有互易性。借助深度学习，许多工作成功实现了大规模MIMO场景下压缩理想CSI的重建。然而，单纯进行CSI重建的实际应用价值有限，因为其未考虑信道估计与目标波束赋形设计。本文提出一种联合优化网络，用于信道估计与反馈，从而学习到具有频谱效率的波束赋形器。此外，采用易于部署的子阵列混合波束赋形架构，并专门设计了一种实用的轻量级端到端网络。实验表明，与现有最优方法相比，所提网络在资源敏感的终端设备上体积缩减超过10倍，且性能损失极小。