Beam Management in Ultra-Dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach-Reference-Cited by-同舟云学术

Beam Management in Ultra-Dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach

Author:

Xue Qing¹^ORCID,Liu Yi-Jing²^ORCID,Sun Yao³^ORCID,Wang Jian²,Yan Li⁴,Feng Gang²^ORCID,Ma Shaodan⁵^ORCID

Affiliation:

1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China

2. National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China

3. James Watt School of Engineering, University of Glasgow, Glasgow, U.K.

4. Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu, China

5. Department of Electrical and Computer Engineering, State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China

Funder

NSFC

Macao Young Scholars Program

China Postdoctoral Science Foundation

Science and Technology Research Program of Chongqing Municipal Education Commission

Project funded by China Postdoctoral Science Foundation

Sichuan Provincial NSFC

School-Enterprise (Huawei) Cooperation Project

Science and Technology Development Fund, Macau SAR

Research Committee of University of Macau

University of Electronic Science and Technology of China and the Chongqing University of Posts and Telecommunications for the work of this paper

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture

Link

Reference36 articles.

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