4 papers
ConsisFormer: Compute-Efficient Transformer for Wireless Foundation Models Based on Channel Consistency
Yuwei Wang, Li Sun, Tingting Yang +4
Wireless foundation models (WFMs) have recently emerged as a promising paradigm for AI-native 6G networks, enabling universal channel representations adaptable to diverse communica…
Lightweight Adaptive Feature Composition for Heterogeneous Downstream Adaptation of Wireless Foundation Models
Yuxuan Shi, Tingting Yang, Kangning Ma +4
Mobile systems increasingly rely on heterogeneous learning-enabled wireless functions, for which separate taskspecific models incur redundant training and model-management overhead…
SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction
Liwen Jing, Yisha Lu, Tingting Yang +5
This paper proposes SpikeWFM, a novel hybrid architecture that integrates spiking neural networks (SNNs) with conventional artificial neural network (ANN)-based transformers for wi…
Filter-and-Attend: Wireless Channel Foundation Model with Noise-Plus-Interference Suppression Structure
Yuwei Wang, Li Sun, Tingting Yang +3
Wireless channel foundation model (WCFM) is a task-agnostic AI model that is pre-trained to learn a universal channel representation for a wide range of communications and sensing…