PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels
The article introduces PilotWiMAE, a self-supervised framework designed for wireless channel representation learning. It addresses the limitations of existing channel models that assume full channel state information. The framework demonstrates improved performance in channel estimation while reducing observation space and latency.
- ▪PilotWiMAE utilizes noisy pilot observations to enhance wireless channel representation learning.
- ▪The framework allows for a significant reduction in observation space and operates effectively without full-CSI availability.
- ▪PilotWiMAE's design incorporates a factorized approach that improves representation quality and enables competitive channel estimation.
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Electrical Engineering and Systems Science > Signal Processing arXiv:2605.22856 (eess) [Submitted on 19 May 2026] Title:PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels Authors:Berkay Guler, Giovanni Geraci, Hamid Jafarkhani View a PDF of the paper titled PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels, by Berkay Guler and 2 other authors View PDF HTML (experimental) Abstract:Channel foundation models assume access to fully observed channels, an assumption that fails in deployment.
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