The design of precoding plays a crucial role in achieving a high downlink sum-rate in multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. In this correspondence, we propose a deep learning based joint CSI feedback and multiuser precoding method in frequency division duplex systems, aiming at maximizing the downlink sum-rate performance in an end-to-end manner. Specifically, the eigenvectors of the CSI matrix are compressed using deep joint source-channel coding techniques. This compression method enhances the resilience of the feedback CSI information against degradation in the feedback channel. A joint multiuser precoding module and a power allocation module are designed to adjust the precoding direction and the precoding power for users based on the feedback CSI information. Experimental results demonstrate that the downlink sum-rate can be significantly improved by using the proposed method, especially in scenarios with low signal-to-noise ratio and low feedback overhead.

翻译：在多用户多输入多输出（MIMO）正交频分复用（OFDM）系统中，预编码设计对于实现高下行总和速率至关重要。本文针对频分双工系统，提出了一种基于深度学习的联合信道状态信息（CSI）反馈与多用户预编码方法，旨在以端到端方式最大化下行总和速率性能。具体而言，利用深度联合信源信道编码技术对CSI矩阵的特征向量进行压缩，该压缩方法增强了反馈CSI信息在反馈信道中抵抗退化的能力。基于反馈CSI信息，设计了联合多用户预编码模块和功率分配模块，用于调整用户的预编码方向与预编码功率。实验结果表明，所提方法能够显著提升下行总和速率，尤其在低信噪比和低反馈开销场景下效果更为突出。