CSI-CLIP++ uses CSI-CIR contrastive alignment to pretrain a channel encoder that improves beam prediction by up to 19.31 percentage points and supports positioning on DeepMIMO data across environments.
WiMamba: Linear-scale wireless foundation model
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Channel intrinsic dimensionality dNL (5-35) sets the scaling ceiling for wireless foundation models, with diminishing returns past ~30M parameters and pilot-aided test-time training on 12M models beating 96M static models by 7-10 dB.
citing papers explorer
-
CSI-CLIP++: A Scalable Channel Foundation Model for Wireless Communication via CIR-CSI Consistency
CSI-CLIP++ uses CSI-CIR contrastive alignment to pretrain a channel encoder that improves beam prediction by up to 19.31 percentage points and supports positioning on DeepMIMO data across environments.
-
How Big Should a Wireless Foundation Model Be?
Channel intrinsic dimensionality dNL (5-35) sets the scaling ceiling for wireless foundation models, with diminishing returns past ~30M parameters and pilot-aided test-time training on 12M models beating 96M static models by 7-10 dB.