CSI-JEPA learns temporal-spectral representations from unlabeled CSI via masked prediction and achieves up to 10.64 percentage points accuracy gain and 98% label savings on seven real-world Wi-Fi sensing tasks.
JEPA-MSAC: A Joint-Embedding Predictive Architec- ture for Multimodal Sensing-Assisted Communications
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Proposes Physical-AI architecture integrating radio-based perception, world modeling, and decision-making for environment-aware 6G networking, with simulations claiming reduced outage and latency versus ISAC.
citing papers explorer
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CSI-JEPA: Towards Foundation Representations for Ubiquitous Sensing with Minimal Supervision
CSI-JEPA learns temporal-spectral representations from unlabeled CSI via masked prediction and achieves up to 10.64 percentage points accuracy gain and 98% label savings on seven real-world Wi-Fi sensing tasks.
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Physical-AI: From Channel Awareness to Environmental Intelligence in 6G Wireless Networks
Proposes Physical-AI architecture integrating radio-based perception, world modeling, and decision-making for environment-aware 6G networking, with simulations claiming reduced outage and latency versus ISAC.