Reducing Pilots in Channel Estimation With Predictive Foundation Models

2025-12-17

AI Summary

To address the challenges of limited pilot overhead, low channel state information (CSI) estimation accuracy, and poor generalization in massive MIMO systems, this paper proposes the first prediction-based foundation model framework for CSI estimation. Our method innovatively adopts a vision transformer architecture pretrained across domains, enabling joint spatiotemporal-frequency modeling and adaptive prior-measurement fusion to achieve efficient pilot encoding and strong cross-scenario transferability. This work pioneers the application of prediction-based foundation models to wireless channel estimation. Extensive evaluations demonstrate significant improvements over conventional and state-of-the-art AI-based methods across diverse deployment scenarios: CSI estimation error is reduced by 42%, pilot overhead is supported below 5%, and robustness to channel noise is substantially enhanced.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Computer Vision: Other Foundations of Computer Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Reduces pilot overhead in channel estimation using predictive models
• Enhances robustness and generalization across diverse wireless scenarios
• Integrates predictive priors with real-time measurements for accurate CSI

Innovation

• Predictive foundation model trained on cross-domain data
• Vision transformer network captures multi-dimensional correlations
• Fusion mechanism integrates predictive priors with real-time measurements