activity
20202022
most citedReinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence

73 citations · 103 across the 9 of their papers we have counts for

collaborators

9 papers

cs.LG2022★ 20 cited

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…

eess.SP2022

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…

cs.MA2022

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…

cs.IT2022★ 1 cited

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…

cs.IT2022★ 73 cited

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…

cs.IT2021★ 1 cited

Environment-Aware Beam Selection for IRS-Aided Communication with Channel Knowledge Map

Dingyang Ding, Di Wu, Yong Zeng +2

Intelligent reflecting surface (IRS)-aided communication is a promising technology for beyond 5G (B5G) systems, to reconfigure the radio environment proactively. However, IRS-aided…

math.OC2021★ 1 cited

Convergence analysis of the discrete consensus-based optimization algorithm with random batch interactions and heterogeneous noises

Dongnam Ko, Seung-Yeal Ha, Shi Jin +1

We present stochastic consensus and convergence of the discrete consensus-based optimization (CBO) algorithm with random batch interactions and heterogeneous external noises. Despi…

cs.DC2021★ 5 cited

Low-Latency Federated Learning over Wireless Channels with Differential Privacy

Kang Wei, Jun Li, Chuan Ma +5

In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. The performance of uploaded models in such situations ca…

eess.SP2020★ 2 cited

Joint Channel Assignment and Power Allocation for Multi-UAV Communication

Lingyun Zhou, Xihan Chen, Mingyi Hong +2

Unmanned aerial vehicle (UAV) swarm has emerged as a promising novel paradigm to achieve better coverage and higher capacity for future wireless network by exploiting the more favo…