Acquiring downlink channel state information (CSI) at the base station is vital for optimizing performance in massive Multiple input multiple output (MIMO) Frequency-Division Duplexing (FDD) systems. While deep learning architectures have been successful in facilitating UE-side CSI feedback and gNB-side recovery, the undersampling issue prior to CSI feedback is often overlooked. This issue, which arises from low density pilot placement in current standards, results in significant aliasing effects in outdoor channels and consequently limits CSI recovery performance. To this end, this work introduces a new CSI upsampling framework at the gNB as a post-processing solution to address the gaps caused by undersampling. Leveraging the physical principles of discrete Fourier transform shifting theorem and multipath reciprocity, our framework effectively uses uplink CSI to mitigate aliasing effects. We further develop a learning-based method that integrates the proposed algorithm with the Iterative Shrinkage-Thresholding Algorithm Net (ISTA-Net) architecture, enhancing our approach for non-uniform sampling recovery. Our numerical results show that both our rule-based and deep learning methods significantly outperform traditional interpolation techniques and current state-of-the-art approaches in terms of performance.

翻译：在基站获取下行信道状态信息对于优化大规模多输入多输出频分双工系统的性能至关重要。尽管深度学习架构已成功促进用户端信道状态信息反馈和基站端信道恢复，但信道状态信息反馈前的欠采样问题常被忽视。该问题源于现行标准中低密度导频部署，导致室外信道出现显著混叠效应，从而限制信道恢复性能。为此，本文提出一种新型基站端信道上采样框架作为后处理方案，以解决欠采样造成的间隙问题。利用离散傅里叶变换平移定理和多径互易性的物理原理，本框架有效利用上行信道状态信息来减轻混叠效应。我们进一步开发了一种基于学习的方法，将所提算法与迭代收缩阈值算法网络架构相结合，增强了对非均匀采样恢复的处理能力。数值结果表明，基于规则的算法和深度学习方法在性能上均显著优于传统插值技术及当前最先进的方法。