End-to-End Beamforming-Oriented CSI Acquisition Framework for RIS-Assisted Networks
Yiming Cui, Jiajia Guo, Chao-Kai Wen, Shi Jin, En Tong · IEEE Transactions on Wireless Communications · 2025
Reconfigurable Intelligent Surfaces (RIS) are an emerging technology that holds significant promise for customizing wireless channels to meet specific communication requirements. Accurate channel state information (CSI) is essential for fully realizing the potential of RIS. However, due to the passive nature of RIS and the large number of reflecting elements, acquiring CSI for the base station (BS)-RIS-user equipment (UE) link presents considerable challenges. In this paper, we propose a deep learning (DL)-based framework for downlink CSI acquisition. Specifically, we introduce a novel DL-based channel estimation framework, termed PPNet, which facilitates efficient pilot transmission. The key innovation of PPNet lies in the joint design and optimization of pilot signals from the BS and phase shifts from the RIS, both represented through neural networks, alongside the channel estimation module. By capturing environment-specific features with neural networks, PPNet enables more efficient utilization of pilot power. Furthermore, we propose a beamforming-oriented CSI acquisition framework, RIS-E2ENet, which jointly optimizes the entire CSI acquisition process, including channel estimation, CSI feedback, and active/passive beamforming design, to enhance CSI acquisition efficiency. To adapt to the dynamic nature of real-world environments, RIS-E2ENet incorporates a lightweight UE-side neural network design, enabling low-overhead online training. Extensive evaluations show that the proposed frameworks improve spectral efficiency by 58.55%, while maintaining minimal pilot and feedback overhead.