PilotWiMAE pretrains an encoder on noisy pilots with factorized attention, 99% masking, patch-normalized reconstruction, scale loss, and AWGN curriculum to outperform supervised baselines in cross-frequency beam selection and channel tasks from 3.5 GHz pretraining to 28 GHz evaluation.
A wireless foundation model for multi-task prediction
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
SiFo pretrains a CSI feedback model on source sites and uses RSRP-based user matching to calibration memory for site-specific subspace guidance at target sites without parameter updates.
AirFM-DDA reparameterizes wireless channel data into the delay-Doppler-angle domain and uses efficient window attention to achieve better zero-shot performance on channel prediction and estimation with lower compute cost.
Wireless data lacks the self-contained tokenized substrate of text, so monolithic wireless world models are unsuitable for 6G; composable agentic systems using specialized components and explicit interfaces are the realistic alternative.
citing papers explorer
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PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels
PilotWiMAE pretrains an encoder on noisy pilots with factorized attention, 99% masking, patch-normalized reconstruction, scale loss, and AWGN curriculum to outperform supervised baselines in cross-frequency beam selection and channel tasks from 3.5 GHz pretraining to 28 GHz evaluation.
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SiFo: Wireless Foundation Model for Low-Overhead Site-Specific CSI Feedback
SiFo pretrains a CSI feedback model on source sites and uses RSRP-based user matching to calibration memory for site-specific subspace guidance at target sites without parameter updates.
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AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G
AirFM-DDA reparameterizes wireless channel data into the delay-Doppler-angle domain and uses efficient window attention to achieve better zero-shot performance on channel prediction and estimation with lower compute cost.
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Against the Monolithic Wireless World Model: Why NextG Needs Composable and Agentic Intelligence
Wireless data lacks the self-contained tokenized substrate of text, so monolithic wireless world models are unsuitable for 6G; composable agentic systems using specialized components and explicit interfaces are the realistic alternative.