arXiv:2512.15562v1 [cs.IT] 17 Dec 2025
Reducing Pilots in Channel Estimation With Predictive Foundation Models
Authors: Xingyu Zhou, Le Liang, Hao Ye, Jing Zhang, Chao-Kai Wen, Shi Jin
This paper introduces a groundbreaking predictive-foundation-model-based framework for Channel State Information (CSI) acquisition in advanced wireless systems. By leveraging large-scale cross-domain data and a novel 'predict-and-refine' strategy, the framework significantly reduces pilot overhead while achieving unprecedented accuracy, robustness, and generalization across diverse propagation environments and system configurations, surpassing traditional and data-driven baselines.
Executive Impact: AI-Native Wireless Performance
Our analysis highlights the transformative potential of Predictive Foundation Models (PFMs) in optimizing critical wireless communication processes, delivering substantial gains in efficiency and reliability for next-generation networks.
0 dB
NMSE Improvement
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BER Reduction (High SNR)
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Pilot Overhead Reduction
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Zero-Shot Generalization Gain
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Predictive Foundation Model Workflow
The proposed 'predict-and-refine' strategy integrates historical CSI prediction with real-time pilot processing to achieve superior channel estimation.
Historical CSI Input (Previous Slot)
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PFM Predicts Current Channel Prior
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Pilot Observations & Pilot Processing Network
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Fusion Module (Prior + Pilots)
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Refined CSI Estimate (Current Slot)
1-5 dB
NMSE Improvement achieved by PFM-aided estimator
| Feature | PFM-aided CE | ViT Baseline | CNN Baseline | LMMSE |
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| BER Reduction (vs. LMMSE) |
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| Low Pilot Density (2P) |
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| High Mobility (300 km/h) |
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> 3 dB
BER Improvement (High SNR) for reliable communication
Case Study: Zero-Shot Generalization Across Unseen Scenarios
The framework's ability to generalize to previously unseen user speeds, antenna configurations, and channel environments without retraining is a testament to the predictive power of foundation models.
Scenario: Unseen Mobility (150 km/h)
Challenge: Rapidly time-varying channels, causing significant degradation for other models.
Solution Impact: PFM-aided CE maintained superior performance (>2 dB NMSE gain at 20 dB SNR), demonstrating robust generalization by learning dynamic temporal representations.
> 4 dB
NMSE Gain in Unseen Antenna Configurations
| Metric | PFM-aided CE | ViT | CNN | LMMSE |
|---|---|---|---|---|
| Inference Latency (ms) | 2.81 | 0.83 | 0.52 | 0.43 |
| Parameters (Millions) | 24.00 | ~1.04 | ~1.04 | / |
Two-Phase Training Strategy
A refined training approach ensures the PFM backbone is adapted to wireless channels, followed by a lighter-weight fine-tuning for the entire estimator, balancing performance and efficiency.
Phase 1: PFM Adaptation (on channel data)
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Phase 2: Estimator Refinement (Fusion & PPN)
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