47 citations · 87 across the 13 of their papers we have counts for
13 papers
A Unified Cloud-Edge-Terminal Framework for Multimodal Integrated Sensing and Communication
Yubo Peng, Luping Xiang, Kun Yang +3
The transition to 6G calls for tightly integrated sensing and communication to support mission-critical services such as autonomous driving, embodied AI, and high-precision telemed…
Large Language Model-Driven Distributed Integrated Multimodal Sensing and Semantic Communications
Yubo Peng, Luping Xiang, Bingxin Zhang +1
Traditional single-modal sensing systems-based solely on either radio frequency (RF) or visual data-struggle to cope with the demands of complex and dynamic environments. Furthermo…
SIMAC: A Semantic-Driven Integrated Multimodal Sensing And Communication Framework
Yubo Peng, Luping Xiang, Kun Yang +3
Traditional single-modality sensing faces limitations in accuracy and capability, and its decoupled implementation with communication systems increases latency in bandwidth-constra…
Semantic Communications with Computer Vision Sensing for Edge Video Transmission
Yubo Peng, Luping Xiang, Kun Yang +2
Despite the widespread adoption of vision sensors in edge applications, such as surveillance, the transmission of video data consumes substantial spectrum resources. Semantic commu…
Attention-based UAV Trajectory Optimization for Wireless Power Transfer-assisted IoT Systems
Li Dong, Feibo Jiang, Yubo Peng
Unmanned Aerial Vehicles (UAVs) in Wireless Power Transfer (WPT)-assisted Internet of Things (IoT) systems face the following challenges: limited resources and suboptimal trajector…
Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance
Li Dong, Yubo Peng, Feibo Jiang +2
In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hen…
GAI-Enabled Explainable Personalized Federated Semi-Supervised Learning
Yubo Peng, Feibo Jiang, Li Dong +2
Federated learning (FL) is a commonly distributed algorithm for mobile users (MUs) training artificial intelligence (AI) models, however, several challenges arise when applying FL…
Personalized Federated Learning for Generative AI-Assisted Semantic Communications
Yubo Peng, Feibo Jiang, Li Dong +2
Semantic Communication (SC) focuses on transmitting only the semantic information rather than the raw data. This approach offers an efficient solution to the issue of spectrum reso…
Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system
Li Dong, Feibo Jiang, Minjie Wang +2
The intelligent reflection surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal…
Personalized Wireless Federated Learning for Large Language Models
Feibo Jiang, Li Dong, Siwei Tu +5
Large language models (LLMs) have driven profound transformations in wireless networks. However, within wireless environments, the training of LLMs faces significant challenges rel…
Large Generative Model Assisted 3D Semantic Communication
Feibo Jiang, Yubo Peng, Li Dong +4
Semantic Communication (SC) is a novel paradigm for data transmission in 6G. However, there are several challenges posed when performing SC in 3D scenarios: 1) 3D semantic extracti…
Large Language Model Enhanced Multi-Agent Systems for 6G Communications
Feibo Jiang, Li Dong, Yubo Peng +5
The rapid development of the Large Language Model (LLM) presents huge opportunities for 6G communications, e.g., network optimization and management by allowing users to input task…
Large AI Model Empowered Multimodal Semantic Communications
Feibo Jiang, Li Dong, Yubo Peng +4
Multimodal signals, including text, audio, image, and video, can be integrated into Semantic Communication (SC) systems to provide an immersive experience with low latency and high…