Improving the Performance of R17 Type-II Codebook with Deep Learning

TL;DR

This paper introduces deep learning techniques to enhance the R17 Type-II codebook for better channel state information feedback, focusing on port selection and CSI reconstruction, leading to improved sum rate performance.

Contribution

The paper proposes two novel deep learning-based methods for R17 Type-II codebook enhancement, addressing port selection under low SNR and CSI reconstruction leveraging sparse structures.

Findings

Improved sum rate performance over traditional methods.

Effective port selection using focal loss in low SNR conditions.

Enhanced CSI reconstruction with a weighted shortcut module.

Abstract

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.