MulViT-TF uses distributed multi-view vision and Transformer fusion to estimate RSSI, cutting RMSE by up to 26.3% versus single-view baselines in two indoor scenes while using fewer resources.
Reading radio from camera: Visually-grounded, lightweight, and interpretable RSSI prediction
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Telecom World Models introduce a three-layer architecture for learned, action-conditioned, uncertainty-aware modeling of 6G network dynamics, combining digital twins and foundation models, with a network slicing proof-of-concept showing improved KPI prediction over baselines.
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Distributed Multi-View Vision-Only RSSI Estimation
MulViT-TF uses distributed multi-view vision and Transformer fusion to estimate RSSI, cutting RMSE by up to 26.3% versus single-view baselines in two indoor scenes while using fewer resources.
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Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G
Telecom World Models introduce a three-layer architecture for learned, action-conditioned, uncertainty-aware modeling of 6G network dynamics, combining digital twins and foundation models, with a network slicing proof-of-concept showing improved KPI prediction over baselines.