6 papers
Experience-Centric Resource Management in ISAC Networks: A Digital Agent-Assisted Approach
Xinyu Huang, Yixiao Zhang, Yingying Pei +2
In this paper, we propose a digital agent (DA)-assisted resource management scheme for enhanced user quality of experience (QoE) in integrated sensing and communication (ISAC) netw…
Large AI Model for Delay-Doppler Domain Channel Prediction in 6G OTFS-Based Vehicular Networks
Jianzhe Xue, Dongcheng Yuan, Zhanxi Ma +4
Channel prediction is crucial for high-mobility vehicular networks, as it enables the anticipation of future channel conditions and the proactive adjustment of communication strate…
Revolutionizing QoE-Driven Network Management with Digital Agents in 6G
Xuemin Shen, Xinyu Huang, Jianzhe Xue +3
In this article, we present a digital agent (DA)-assisted network management framework for future sixth generation (6G) networks considering user quality of experience (QoE). A nov…
Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles
Jianzhe Xue, Dongcheng Yuan, Yu Sun +5
The growing number of connected vehicles offers an opportunity to leverage internet of vehicles (IoV) data for traffic state estimation (TSE) which plays a crucial role in intellig…
ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited Data
Doncheng Yuan, Jianzhe Xue, Jinshan Su +2
Traffic flow estimation (TFE) is crucial for urban intelligent traffic systems. While traditional on-road detectors are hindered by limited coverage and high costs, cloud computing…
Spatial-Temporal Generative AI for Traffic Flow Estimation with Sparse Data of Connected Vehicles
Jianzhe Xue, Yunting Xu, Dongcheng Yuan +4
Traffic flow estimation (TFE) is crucial for intelligent transportation systems. Traditional TFE methods rely on extensive road sensor networks and typically incur significant cost…