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.
Large wireless localization model (LWLM): A foundation model for positioning in 6G networks
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
PilotWiMAE pretrains an encoder on noisy pilots with factorized attention, 99% masking, patch-normalized reconstruction, scale loss, and AWGN curriculum to outperform supervised baselines in cross-frequency beam selection and channel tasks from 3.5 GHz pretraining to 28 GHz evaluation.
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.
AirFM-DDA reparameterizes wireless channel data into the delay-Doppler-angle domain and uses efficient window attention to achieve better zero-shot performance on channel prediction and estimation with lower compute cost.
OmniLoc presents a geometry-aware foundation model for anchor-free user equipment localization from wireless signals that claims to generalize across diverse indoor environments.
A pretrained wireless foundation model claims to unify zero-shot channel reconstruction and few-shot adaptation across 9 CSI tasks, outperforming task-specific supervised baselines.
Surveys adaptation of foundation models to wireless tasks across off-the-shelf, wireless-native, and agentic paradigms for 6G PHY intelligence and network autonomy.
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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PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels
PilotWiMAE pretrains an encoder on noisy pilots with factorized attention, 99% masking, patch-normalized reconstruction, scale loss, and AWGN curriculum to outperform supervised baselines in cross-frequency beam selection and channel tasks from 3.5 GHz pretraining to 28 GHz evaluation.
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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.
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AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G
AirFM-DDA reparameterizes wireless channel data into the delay-Doppler-angle domain and uses efficient window attention to achieve better zero-shot performance on channel prediction and estimation with lower compute cost.
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OmniLoc: A Geometry-Aware Foundation Model for Anchor-Free UE Localization Across Diverse Indoor Environments
OmniLoc presents a geometry-aware foundation model for anchor-free user equipment localization from wireless signals that claims to generalize across diverse indoor environments.
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WiFo-2: a generalist foundation model unifies heterogeneous wireless system design
A pretrained wireless foundation model claims to unify zero-shot channel reconstruction and few-shot adaptation across 9 CSI tasks, outperforming task-specific supervised baselines.
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Foundation Models for Wireless Communications: From PHY Intelligence to Network Autonomy
Surveys adaptation of foundation models to wireless tasks across off-the-shelf, wireless-native, and agentic paradigms for 6G PHY intelligence and network autonomy.