A Perception vs. Distortion Perspective on Score-Based Generative Channel Estimation

The paper presents a theoretical analysis of score-based generative models for channel estimation in wireless communications, addressing their advantages and limitations compared to traditional discriminative learning methods. It establishes a perception-distortion tradeoff framework, demonstrating that score-based estimation can significantly reduce excess risk in high uncertainty scenarios, enabling near Bayesian-optimal precoding. This research is crucial for practitioners as it provides insights into when to leverage score-based methods for improved performance in wireless tasks, particularly in varying predictive uncertainty conditions.