4 papers · 1 filter
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…
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…
SpecFed: Accelerating Federated LLM Inference with Speculative Decoding and Compressed Transmission
Ce Zheng, Xinghan Wang, Jiahong Ning +3
Federated inference enhances LLM performance in edge computing through weighted averaging of distributed model predictions. However, autoregressive LLM inference requires frequent…
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…