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Deep learning based channel state information (CSI) feedback in frequency
division duplex systems has drawn widespread attention in both academia and
industry. In this paper, we focus on integrating the Type-II codebook in the
wireless communication standards with deep learning to enhance the performance
of CSI feedback. In contrast to the existing deep learning based studies on the
Release 16 Type-II codebook, 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 CSI, where the performance of deep learning based
conventional 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. Furthermore, a weighted shortcut
module is designed to facilitate the accurate reconstruction, and a two-stage
loss function that combines the mean squared error and sum rate is proposed for
adapting to practical multi-user scenarios. Simulation results demonstrate that
our proposed deep learning based port selection and CSI reconstruction methods
can improve the sum rate performance compared with the traditional R17 Type-II
codebook and deep learning benchmarks.
