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.
MUSE-FM: Multi-task environment-aware foundation model for wireless communications
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
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.
SpikeWFM integrates spiking neurons into ANN transformers for wireless foundation models, claiming better pre-training convergence and channel prediction accuracy under noise.
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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SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction
SpikeWFM integrates spiking neurons into ANN transformers for wireless foundation models, claiming better pre-training convergence and channel prediction accuracy under noise.