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20112026
most citedRethinking Modern Communication from Semantic Coding to Semantic Communication

142 citations · 487 across the 83 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

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.…

quant-ph2022★ 14 cited

Validating quantum-supremacy experiments with exact and fast tensor network contraction

Yong Liu, Yaojian Chen, Chu Guo +11

The quantum supremacy experiment, such as Google Sycamore [Nature \textbf{574}, 505 (2019)], poses great challenge for classical verification due to the exponentially-increasing co…

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…

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…