发表机构
Beijing University of Posts and Telecommunications; ZGC Institute of Ubiquitous-X Innovation and Applications; Shanghai Jiao Tong University（北京邮电大学; 中关村泛在X创新与应用研究院; 上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对MIMO语义通信中信道和均衡问题，提出基于恢复流匹配的框架，含CRFM和SRFM模块，通过双锚点扰动训练策略增强鲁棒性，经ODE求解器推理，实验证明提高了相关指标且采样步骤少。
AI 中文摘要
在多输入多输出（MIMO）语义通信中，不完美的信道状态信息（CSI）和均衡失配会严重降低语义重建质量。为解决此问题，我们提出了一个基于统一恢复流匹配（RFM）的框架用于信道细化和均衡校正。具体而言，开发了信道RFM（CRFM）模块来细化粗糙信道，提高信道估计精度。基于细化后的信道，使用语义RFM（SRFM）模块校正均衡后潜在空间中的残余失真。关键是将信道估计和均衡这两个级联的逆问题表述为统一的条件恢复任务，其中学习到的条件速度场引导扰动分布趋向目标分布。为增强这两个模块在各种失真条件下的鲁棒性，我们开发了双锚点扰动训练策略，联合学习近流形细化和大误差校正，并通过几步确定性常微分方程（ODE）求解器进行推理。在MIMO信道和视觉语义传输任务上的大量实验表明，该方案提高了信道估计和语义重建质量的关键指标。此外，与基于扩散的代表性生成基线相比，该方法所需的采样步骤更少。
英文摘要
In multiple-input multiple-output (MIMO) semantic communication, imperfect channel state information (CSI) and equalization mismatch can seriously degrade semantic reconstruction quality. To address this issue, we propose a unified restoration flow matching (RFM)-based framework for channel refinement and equalization correction. Specifically, the channel RFM (CRFM) module is developed to refine the coarse channel, thereby improving channel estimation accuracy. Based on the refined channel, the developed semantic RFM (SRFM) module is employed to correct the residual distortions in the post-equalization latent space. The key idea is to formulate the two cascaded inverse problems of channel estimation and equalization as the unified conditional restoration task, in which the learned conditional velocity field guides the perturbed distribution towards the target distribution. To enhance the robustness of these two modules under various distortion conditions, we develop a dual-anchor perturbation training strategy that jointly learns near-manifold refinement and large-error correction, and implement inference through a few-step deterministic ordinary differential equation (ODE) solver. Extensive experiments on MIMO channels and visual semantic transmission tasks demonstrate that the proposed scheme improves key metrics for channel estimation and semantic reconstruction quality. Moreover, compared with representative diffusion-based generative baselines, the proposed method requires fewer sampling steps.
