  Deep learning based channel state information (CSI) feedback in frequency
division duplex systems has drawn much attention in both academia and industry.
In this paper, we focus on integrating the Type-II codebook in the beyond
fifth-generation (B5G) wireless systems with deep learning to enhance the
performance of CSI feedback. In contrast to its counterpart in Release 16, the
Type-II codebook in Release 17 (R17) exploits the angular-delay-domain partial
reciprocity between uplink and downlink channels and selects part of
angular-delay-domain ports for measuring and feeding back the downlink CSI,
where the performance of the conventional deep learning methods is limited due
to the deficiency of sparse structures. To address this issue, we propose the
new paradigm of adopting deep learning to improve the performance of R17
Type-II codebook. Firstly, considering the relatively low signal-to-noise ratio
of uplink channels, deep learning is utilized to refine the selection of the
dominant angular-delay-domain ports, where the focal loss is harnessed to solve
the class imbalance problem. Secondly, we propose to reconstruct the downlink
CSI by way of deep learning based on the feedback of R17 Type-II codebook at
the base station, where the information of sparse structures can be effectively
leveraged. Finally, a weighted shortcut module is designed to facilitate the
accurate reconstruction, and a two-stage loss function with the combination of
the mean squared error and sum rate is proposed for adapting to actual
multi-user scenarios. Simulation results demonstrate that our proposed
angular-delay-domain port selection and CSI reconstruction paradigm can improve
the sum rate performance by more than 10% compared with the traditional R17
Type-II codebook and deep learning benchmarks.
