CSI RefineNet：一种用于高移动性 OFDM 系统的软数据辅助迭代接收机
CSI RefineNet: A Soft Data-Aided Iterative Receiver for High-Mobility OFDM Systems
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中文总结 AI 辅助
针对高移动性 OFDM 系统中 CSI 不准确问题，提出 CSI RefineNet 接收机，通过软符号决策迭代细化 CSI，经导频驱动初始化、构建软数据辅助信道观测等步骤形成迭代结构，还有两阶段训练策略，仿真显示其 BER 性能优且鲁棒性强。
中文摘要 AI 辅助
在高移动性正交频分复用（OFDM）系统中，快速的信道变化会使从导频估计出的信道状态信息（CSI）对于数据子载波不准确，导致与其有效信道不匹配。为解决此问题，本文提出一种 CSI RefineNet 接收机，它以数据辅助方式利用软符号决策迭代地细化 CSI。具体而言，首先采用导频驱动初始化模块获得粗略的 CSI 估计及相应符号后验概率。基于这些后验概率，在所有子载波上构建软数据辅助信道观测并与初始 CSI 融合以细化信道估计。细化后的 CSI 随后反馈到均衡和检测模块，形成迭代接收机结构。为提高训练稳定性并充分利用细化能力，还开发了两阶段训练策略。仿真结果表明，所提出的 CSI RefineNet 接收机在高移动性 OFDM 系统中不同速度、调制阶数和导频间隔配置下实现了卓越的误码率性能和强大的鲁棒性。
英文摘要
In high-mobility orthogonal frequency division multiplexing (OFDM) systems, rapid channel variation can make the channel state information (CSI) estimated from pilots inaccurate for data subcarriers, leading to a mismatch with their effective channel. To address this issue, this paper proposes a CSI RefineNet receiver, where the CSI is iteratively refined using soft symbol decisions in a data-aided manner. Specifically, a pilot-driven initialization module is first employed to obtain a coarse CSI estimation and the corresponding symbol posterior probabilities. Based on these posteriors, soft data-aided channel observations are constructed over all subcarriers and fused with the initial CSI to refine the channel estimation. The refined CSI is subsequently fed back to the equalization and detection modules, thereby forming an iterative receiver structure. To improve training stability and fully exploit the refinement capability, a two-stage training strategy is also developed. Simulation results demonstrate that the proposed CSI RefineNet receiver achieves superior BER performance and strong robustness under different velocities, modulation orders, and pilot spacing configurations in high-mobility OFDM systems.
