Deep Learning Empowered CSI Acquisition and Feedback for B5G Wireless Systems

Deep learning based channel state information (CSI) acquisition and feedback in frequency division duplex systems have drawn much attention in the beyond fifth-generation (B5G) wireless systems. In this paper, we focus on exploiting the CSI codebook in B5G wireless standards with deep learning to enhance the performance of CSI acquisition and feedback. Specifically, the angular-delay-domain partial reciprocity between uplink and downlink channels is considered, and part of angular-delay-domain ports are selected 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 CSI 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 CSI 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 standard CSI codebook and traditional deep learning benchmarks.