Uplink-Assisted Joint Channel Estimation and CSI Feedback: An Approach Based on Deep Joint Source-Channel Coding
Year: 2026
Type: article
Abstract: 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 CSI feedback approach via deep joint source-channel coding (DJSCC) architecture for downlink CSI acquisition, which mitigates performance degradation caused by distribution bias across separately trained modules in traditional modular communication frameworks and the “cliff effect” encountered in the conventional separate source-channel coding. The impact of uplink channel estimation errors on system performance is also analyzed, and a novel error-aware end-to-end training strategy is proposed to mitigate their adverse effects. 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-to-end multi-module joint training architecture are validated through comprehensive ablation and scalability experiments.
Authors: Yiran Guo, Wei Chen, Bo Ai
Institution: Beijing Jiaotong University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7140075601