Towards Efficient Subarray Hybrid Beamforming: Attention Network-based Practical Feedback in FDD Massive MU-MIMO Systems
This work addresses efficiency and practicality in wireless communication systems, specifically for FDD massive MU-MIMO, but is incremental as it builds on existing deep learning approaches with architectural improvements.
The paper tackled the problem of channel state information feedback in FDD massive MU-MIMO systems by introducing a jointly optimized network for channel estimation and feedback to enable spectral-efficient beamforming, achieving a network over 10 times lighter at user equipment with only minor performance loss compared to previous state-of-the-art methods.
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.