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.
Filter-and-attend: Wireless channel foundation model with noise-plus-interference suppression structure
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
eess.SP 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Surveys adaptation of foundation models to wireless tasks across off-the-shelf, wireless-native, and agentic paradigms for 6G PHY intelligence and network autonomy.
citing papers explorer
-
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.
-
Foundation Models for Wireless Communications: From PHY Intelligence to Network Autonomy
Surveys adaptation of foundation models to wireless tasks across off-the-shelf, wireless-native, and agentic paradigms for 6G PHY intelligence and network autonomy.