20 citations · 29 across the 7 of their papers we have counts for
5 papers · 1 filter
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control
Ziyan Yin, Zhe Wang, Jun Li +3
The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks. To enhance the network capacity and reliability, we propos…
Control-Oriented Power Allocation for Integrated Satellite-UAV Networks
Chengleyang Lei, Wei Feng, Jue Wang +2
This letter presents a sensing-communication-computing-control (SC3) integrated satellite unmanned aerial vehicle (UAV) network, where the UAV is equipped with on-board sensors, mo…
Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning
Jie Zhang, Jun Li, Yijin Zhang +5
Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by coo…
Active IRS Aided Multiple Access for Energy-Constrained IoT Systems
Guangji Chen, Qingqing Wu, Chong He +3
We investigate the fundamental multiple access (MA) scheme in an active intelligent reflecting surface (IRS) aided energy-constrained Internet-of-Things (IoT) system, where an acti…
Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence
Peng Wei, Kun Guo, Ye Li +5
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertaint…