RA-LWLM uses retrieval from per-scene databases and in-context learning with a frozen foundation model to achieve cross-scene wireless localization without retraining.
CSI2Vec: Towards a universal CSI feature representation for positioning and channel charting,
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LatentWave applies JEPA pretraining to wireless data for more transferable representations than masked reconstruction baselines, with evaluations on RF classification, 5G positioning, beam prediction, and LoS/NLoS tasks.
citing papers explorer
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RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models
RA-LWLM uses retrieval from per-scene databases and in-context learning with a frozen foundation model to achieve cross-scene wireless localization without retraining.
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LatentWave: JEPA Pretraining for Wireless Foundation Models
LatentWave applies JEPA pretraining to wireless data for more transferable representations than masked reconstruction baselines, with evaluations on RF classification, 5G positioning, beam prediction, and LoS/NLoS tasks.