Machine Learning-Aided Dual CSI Feedback in Next Generation WLANs

Givens rotation based channel state information (CSI) feedback has been adopted as a limited feedback technique for beamforming in the wireless local area networks (WLANs). On the other hand, the long term evolution (LTE) systems utilize the predefined codebook for the CSI feedback. In this paper, we propose a dual CSI feedback technique for next generation WLANs, which combines the codebook and Givens rotation to derive the benefits of both techniques. Machine learning (ML) technique is adopted for the improved codebook design. The extensive simulation is carried out via IEEE 802.11be link-level simulator to verify the performance of the proposed scheme. It shows that the proposed scheme can reduce feedback overhead by more than 50% compared to the scheme adopted in current WLANs, and enhances the throughput significantly.