A Perception vs. Distortion Perspective on Score-Based Generative Channel Estimation
2026-06-15 • Artificial Intelligence
Artificial IntelligenceMachine Learning
AI summaryⓘ
The authors explore how score-based models, known for their success in computer vision, can be used for estimating wireless channels, a key challenge in wireless communication. They explain when these models perform better than traditional methods that directly minimize estimation errors. Their analysis shows that score-based methods work best when there is a lot of uncertainty in predictions, helping to reduce risk in subsequent wireless tasks like signal transmission. However, when uncertainty is low, simpler traditional methods are more efficient. The authors support their findings with theoretical arguments and extensive simulations.
score-based modelschannel estimationwireless communicationsperception-distortion tradeoffinverse problemsdiscriminative learningBayesian optimalitypredictive uncertaintyprecoding
Authors
Marco Skocaj, Lukas Eller, Mate Boban
Abstract
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.