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Channel state information (CSI) feedback is necessary for the frequencydivision duplexing (FDD) multiple input multiple output (MIMO) systems due tothe channel non-reciprocity. With the help of deep learning, many works havesucceeded in rebuilding the compressed ideal CSI for massive MIMO. However,simple CSI reconstruction is of limited practicality since the channelestimation and the targeted beamforming design are not considered. In thispaper, a jointly optimized network is introduced for channel estimation andfeedback so that a spectral-efficient beamformer can be learned. Moreover, thedeployment-friendly subarray hybrid beamforming architecture is applied and apractical lightweight end-to-end network is specially designed. Experimentsshow that the proposed network is over 10 times lighter at theresource-sensitive user equipment compared with the previous state-of-the-artmethod with only a minor performance loss.

Towards Efficient Subarray Hybrid Beamforming: Attention Network-based Practical Feedback in FDD Massive MU-MIMO Systems