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

TL;DR

This paper proposes a lightweight, jointly optimized deep learning network for channel estimation and feedback in FDD massive MIMO systems, enabling efficient hybrid beamforming with minimal resource use at user equipment.

Contribution

It introduces a practical, end-to-end network tailored for subarray hybrid beamforming, improving efficiency and reducing resource requirements in FDD massive MIMO systems.

Findings

Over 10 times lighter network at user equipment

Minor performance loss compared to state-of-the-art

Effective joint channel estimation and feedback

Abstract

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.