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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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.22856 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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