Channel configuration improves radio foundation model embeddings
Original title: Beyond Channel Data: Configuration-Enriched Radio Foundation Models
CFG-enriched embeddings beat or match baselines for line-of-sight classification, timing advance and beam selection, including unseen configurations.
The paper adds channel configuration information to radio foundation model pre-training and tests the embeddings as inductive priors when labels are scarce. Comparisons use raw channel tokens and several baselines across three data regimes. Tasks are line-of-sight classification, timing-advance estimation and beam selection, in both in-distribution and out-of-distribution configuration settings. The authors report consistent gains or competitive results for seen and unseen configurations.
Unconfirmed: The abstract does not name the baseline models or report numeric scores.
- Evaluation covers in-distribution and out-of-distribution channel configurations.
- Three label-scarcity data regimes are compared.
- Baselines include raw channel tokens and multiple other models.
- The abstract states no numeric accuracy deltas.
From the original article
1 min read at arxiv.orgThis paper proposes incorporating channel configuration (CFG) information into Radio Foundation Model (RFM) pre-training and demonstrates that the resulting CFG-enriched embeddings serve as effective inductive priors for downstream tasks under label scarcity.
By Shrihari Vasudevan · arxiv.org
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