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.
LVM4CSI: Enabling direct application of pre-trained large vision models for wireless channel tasks
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
ComHymba introduces a domain-informed wireless foundation model with Hymba blocks for linear-complexity CSI modeling, reporting accuracy gains on eight downstream tasks and up to 3.3x inference speedup over Transformers.
SPA-MAE adapts an MAE backbone with a physical prior module providing parameter-aware and structure-aware guidance to pretrain on CSI data, yielding better downstream performance than prior CSI foundation models with fewer parameters.
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.
Adaptive 3D-RoPE adapts rotary positional encoding to wireless channel physics via learnable 3D frequencies and dynamic CSI control, yielding up to 10.7 dB NMSE gains in scale extrapolation and 1 dB in zero-shot tasks.
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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ComHymba: Low-Complexity Domain-Informed Foundation Model for Wireless Communications
ComHymba introduces a domain-informed wireless foundation model with Hymba blocks for linear-complexity CSI modeling, reporting accuracy gains on eight downstream tasks and up to 3.3x inference speedup over Transformers.
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SPA-MAE: A Physics-Guided CSI Foundation Model for Wireless Physical Layer
SPA-MAE adapts an MAE backbone with a physical prior module providing parameter-aware and structure-aware guidance to pretrain on CSI data, yielding better downstream performance than prior CSI foundation models with fewer parameters.
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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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Adaptive 3D-RoPE: Physics-Aligned Rotary Positional Encoding for Wireless Foundation Models
Adaptive 3D-RoPE adapts rotary positional encoding to wireless channel physics via learnable 3D frequencies and dynamic CSI control, yielding up to 10.7 dB NMSE gains in scale extrapolation and 1 dB in zero-shot tasks.