想看分数模型在无线通信里到底有什么用吗？这篇论文用感知-失真框架告诉你，信道估计时高不确定性用得分匹配，低不确定性用传统方法，有理论有实验。
该论文从感知-失真权衡角度理论分析了分数匹配模型在无线信道估计中的适用条件。通过将下游无线任务（如容量最大化）建模为信道估计的泛函，量化了传统失真最小化方法的超额风险。数值实验表明，在高预测不确定性下，分数生成模型能实现接近贝叶斯最优的预编码，而低不确定性时判别性方法更优。该研究基于IEEE场景验证了超过5dB的信噪比增益。
A Perception vs. Distortion Perspective on Score-Based Generative Channel Estimation
Driven by their remarkable success in computer vision and inverse problem solving, score-based models are increasingly applied to wireless communications, where they show promise across a range of physical-layer tasks. However, despite this growing interest, the current literature often lacks a rigorous analysis of when score-matching offers a tangible advantage over traditional discriminative learning. This paper aims to address this gap through the use-case of channel estimation, a fundamental inverse problem in wireless systems. We present a theoretically grounded interpretation of score-based channel estimation through the lens of the perception-distortion tradeoff, identifying the conditions where score matching excels as well as its key limitations. In particular, by modeling downstream wireless tasks (e.g., capacity maximization) as functionals of the channel estimation process, we quantify the excess risk incurred by standard distortion-minimization approaches. Extensive numerical results show that under high predictive uncertainty, the large excess risk gap can be offset by score-based estimation, enabling near Bayesian-optimal precoding via the learned posterior, whereas in the low predictive uncertainty regime, discriminative distortion-minimization approaches are preferable due to lower complexity and more efficient use of model capacity.