64 citations · 85 across the 29 of their papers we have counts for
7 papers · 1 filter
Fluid Antennas Enabled UAV Communication Systems: Fundamentals, Challenges, and Future Directions
Wenchao Liu, Weijie Yuan, Jun Wu +3
Low-altitude wireless networks (LAWNs) require uncrewed aerial vehicle (UAV) communication systems to adapt to large-scale propagation changes and fast local channel fluctuations.…
SemVideoSte: Agentic AI for Cross-Modal Semantic Video Steganography Communication
Rui Meng, Song Gao, Tingting Zhu +6
We propose an agentic artificial intelligence (AI)-driven cross-modal semantic video steganography communication (SemVideoSte) scheme for secure cross-modal information transmissio…
Logit-Aware MIMO AirComp for Distributed Mixture-of-Experts LLM Inference over Wireless Edge Networks
Lyutianyang Zhang, Yunjian Jia, Liu Cao +3
Distributed mixture-of-experts (MoE) inference is a promising architecture for deploying large language models (LLMs) at wireless edge networks because sparse experts can be placed…
ReFPO: Reflow Regularization for Flow Matching Policy Gradients
Ge Wang, Yibo Peng, Fan Feng +10
We present Reflow-regularized Flow Matching Policy Gradients (ReFPO), a simple online RL method that adds explicit Reflow regularization to FPO for efficient flow-based control. We…
Survey-Free Radio Map Construction via HMM-Based Coarse-to-Fine Inference
Zheng Xing, Weibing Zhao, Guanghui Zhang +6
Traditional radio map construction methods mandate labor-intensive data collection and precise location labeling. To address these limitations, we propose a novel survey-free appro…
Annotation-Free Indoor Radio Mapping via Physics-Informed Trajectory Inference
Zheng Xing, Mengru Wu, Yi Zhang +6
Constructing indoor radio maps traditionally requires extensive site surveys with precise user-location labels, making the calibration process costly and time-consuming. Existing c…
SVLL: Staged Vision-Language Learning for Physically Grounded Embodied Task Planning
Yuyuan Yang, Junkun Hong, Hongrong Wang +11
Embodied task planning demands vision-language models to generate action sequences that are both visually grounded and causally coherent over time. However, existing training parad…