Deep Learning Empowered Type-II Codebook: New Paradigm for Enhancing CSI Feedback

TL;DR

This paper introduces a deep learning-based approach to enhance Type-II CSI feedback in B5G wireless systems, improving port selection and CSI reconstruction to boost sum rate performance.

Contribution

It proposes a novel deep learning paradigm for R17 Type-II codebook, addressing sparse structure limitations and improving CSI feedback accuracy in multi-user scenarios.

Findings

Improves sum rate performance by over 10%

Enhances port selection accuracy in low SNR conditions

Effective CSI reconstruction leveraging sparse structures

Abstract

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.