activity
20092022
most citedOn the Limits of Predictability in Real-World Radio Spectrum State Dynamics: From Entropy Theory to 5G Spectrum Sharing

146 citations · 673 across the 49 of their papers we have counts for

collaborators

50 papers

cs.HC2022★ 12 cited

Semantics-Empowered Communication: A Tutorial-cum-Survey

Zhilin Lu, Rongpeng Li, Kun Lu +4

Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.…

cs.AI2022★ 3 cited

Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning

Xianfu Chen, Zhifeng Zhao, Shiwen Mao +3

The age of information metric fails to correctly describe the intrinsic semantics of a status update. In an intelligent reflecting surface-aided cooperative relay communication sys…

cs.LG2022

AoI-based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching

Jianhang Zhu, Rongpeng Li, Guoru Ding +4

Along with the fast development of network technology and the rapid growth of network equipment, the data throughput is sharply increasing. To handle the problem of backhaul bottle…

cs.NI2022

6G Network AI Architecture for Everyone-Centric Customized Services

Yang Yang, Mulei Ma, Hequan Wu +51

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to…

eess.SY2022★ 2 cited

Adaptive Bit Rate Control in Semantic Communication with Incremental Knowledge-based HARQ

Qingyang Zhou, Rongpeng Li, Zhifeng Zhao +2

Semantic communication has witnessed a great progress with the development of natural language processing (NLP) and deep learning (DL). Although existing semantic communication tec…

cs.LG2022

Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning

Xing Xu, Rongpeng Li, Zhifeng Zhao +1

The paper considers independent reinforcement learning (IRL) for multi-agent decision-making process in the paradigm of federated learning (FL). We show that FL can clearly improve…