Improving the Performance of R17 Type-II Codebook with Deep Learning
Ke Ma Yiliang Sang Yang Ming Jin Lian Chang Tian Zhaocheng Wang
The Type-II codebook in Release 17 (R17) exploits the angular-delay-domain
partial reciprocity between uplink and downlink channels to select part of
angular-delay-domain ports for measuring and feeding back the downlink channel
state information (CSI), where the performance of existing deep learning
enhanced CSI feedback methods is limited due to the deficiency of sparse
structures. To address this issue, we propose two new perspectives of adopting
deep learning to improve the R17 Type-II codebook. Firstly, considering the low
signal-to-noise ratio of uplink channels, deep learning is utilized to
accurately select the dominant angular-delay-domain ports, where the focal loss
is harnessed to solve the class imbalance problem. Secondly, we propose to
adopt deep learning to reconstruct the downlink CSI based on the feedback of
the R17 Type-II codebook at the base station, where the information of sparse
structures can be effectively leveraged. Besides, a weighted shortcut module is
designed to facilitate the accurate reconstruction. Simulation results
demonstrate that our proposed methods could improve the sum rate performance
compared with its traditional R17 Type-II codebook and deep learning
benchmarks.