3 citations · 3 across the 5 of their papers we have counts for
5 papers
Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach
Xiaowen Ye, Yuyi Mao, Xianghao Yu +3
This paper studies an integrated sensing and communications (ISAC) system for low-altitude economy (LAE), where a ground base station (GBS) provides communication and navigation se…
Digital Twin Enhanced Deep Reinforcement Learning for Intelligent Omni-Surface Configurations in MU-MIMO Systems
Xiaowen Ye, Xianghao Yu, Liqun Fu
Intelligent omni-surface (IOS) is a promising technique to enhance the capacity of wireless networks, by reflecting and refracting the incident signal simultaneously. Traditional I…
Data-Driven Online Resource Allocation for User Experience Improvement in Mobile Edge Clouds
Liqun Fu, Jingwen Tong, Tongtong Lin +1
As the cloud is pushed to the edge of the network, resource allocation for user experience improvement in mobile edge clouds (MEC) is increasingly important and faces multiple chal…
From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection
Jingwen Tong, Zhenzhen Chen, Liqun Fu +2
Federated learning (FL) is an appealing paradigm for learning a global model among distributed clients while preserving data privacy. Driven by the demand for high-quality user exp…
Spatial Deep Learning for Site-Specific Movement Optimization of Aerial Base Stations
Jiangbin Lyu, Xu Chen, Jiefeng Zhang +1
Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations (ABSs) to provide wireless connectivity for ground users (GUs) in various emergency scenarios. However, it i…