In frequency division duplex (FDD) multiple-input multiple-output (MIMO) wireless communication systems, the acquisition of downlink channel state information (CSI) is essential for maximizing spatial resource utilization and improving system spectral efficiency. The separate design of modules in AI-based CSI feedback architectures under traditional modular communication frameworks, including channel estimation (CE), CSI compression and feedback, leads to sub-optimal performance. In this paper, we propose an uplink assisted joint CE and and CSI feedback approach via deep learning for downlink CSI acquisition, which mitigates performance degradation caused by distribution bias across separately trained modules in traditional modular communication frameworks. The proposed network adopts a deep joint source-channel coding (DJSCC) architecture to mitigate the cliff effect encountered in the conventional separate source-channel coding. Furthermore, we exploit the uplink CSI as auxiliary information to enhance CSI reconstruction accuracy by leveraging the partial reciprocity between the uplink and downlink channels in FDD systems, without introducing additional overhead. The effectiveness of uplink CSI as assisted information and the necessity of an end-toend multi-module joint training architecture is validated through comprehensive ablation and scalability experiments.

本文针对频分双工MIMO无线通信系统中的下行信道状态信息(CSI)获取问题，提出了一种基于深度学习的上行辅助联合信道估计和CSI反馈方法。该方法通过深度联合源信道编码架构，克服了传统模块化通信框架中模块独立训练导致的性能下降，并通过利用上行CSI作为辅助信息，提高了CSI重建的准确性，展示了在不增加额外开销的情况下有效性。

上行辅助的联合信道估计和CSI反馈: 基于深度联合源信道编码的方法