Reducing Pilots in Channel Estimation with Predictive Foundation Models
This work addresses the problem of efficient and reliable channel estimation for wireless communication systems, representing an incremental improvement over prior AI-based solutions by enhancing robustness and cross-scenario transferability.
The paper tackles the challenge of accurate channel state information acquisition in wireless systems with large antenna arrays and limited pilot overhead by introducing a predictive-foundation-model-based framework, which significantly outperforms existing methods in accuracy, robustness, and generalization across diverse configurations.
Accurate channel state information (CSI) acquisition is essential for modern wireless systems, which becomes increasingly difficult under large antenna arrays, strict pilot overhead constraints, and diverse deployment environments. Existing artificial intelligence-based solutions often lack robustness and fail to generalize across scenarios. To address this limitation, this paper introduces a predictive-foundation-model-based channel estimation framework that enables accurate, low-overhead, and generalizable CSI acquisition. The proposed framework employs a predictive foundation model trained on large-scale cross-domain CSI data to extract universal channel representations and provide predictive priors with strong cross-scenario transferability. A pilot processing network based on a vision transformer architecture is further designed to capture spatial, temporal, and frequency correlations from pilot observations. An efficient fusion mechanism integrates predictive priors with real-time measurements, enabling reliable CSI reconstruction even under sparse or noisy conditions. Extensive evaluations across diverse configurations demonstrate that the proposed estimator significantly outperforms both classical and data-driven baselines in accuracy, robustness, and generalization capability.