Improving the Performance of R17 Type-II Codebook with Deep Learning
This work addresses a domain-specific problem in wireless communication systems by incrementally improving CSI feedback efficiency for 5G/6G networks.
The paper tackled the limited performance of deep learning enhanced CSI feedback in R17 Type-II codebooks by proposing two deep learning approaches: one for accurately selecting dominant angular-delay-domain ports using focal loss to handle class imbalance, and another for reconstructing downlink CSI with a weighted shortcut module, resulting in improved sum rate performance compared to traditional and deep learning benchmarks.
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.