Machine Learning for Large-Scale Optimization in 6G Wireless Networks-Reference-Cited by-同舟云学术

Machine Learning for Large-Scale Optimization in 6G Wireless Networks

Published:2023 Issue:4 Volume:25 Page:2088-2132
ISSN:1553-877X
Container-title:IEEE Communications Surveys & Tutorials
language:
Short-container-title:IEEE Commun. Surv. Tutorials

Author:

Shi Yandong¹,Lian Lixiang²^ORCID,Shi Yuanming²^ORCID,Wang Zixin²^ORCID,Zhou Yong²^ORCID,Fu Liqun³^ORCID,Bai Lin⁴^ORCID,Zhang Jun⁵^ORCID,Zhang Wei⁶^ORCID

Affiliation:

1. Department of Mobile Communications and Terminal Research, China Telecom Research Institute, Guangzhou, China

2. School of Information Science and Technology, ShanghaiTech University, Shanghai, China

3. School of Informatics and the Key Laboratory of Underwater Acoustic Communication and Marine Information Technology (Ministry of Education), Xiamen University, Xiamen, China

4. School of Cyber Science and Technology, Beihang University, Beijing, China

5. Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China

6. School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW, Australia

Funder

National Nature Science Foundation of China

Natural Science Foundation of Shanghai

Shanghai Rising-Star Program

National Natural Science Foundation of China

Research Impact Fund (RIF) from Hong Kong RGC

Special Project for Research and Development in Key areas of Guangdong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/9739/10324457/10198239.pdf?arnumber=10198239

Reference287 articles.

1. Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

2. Federated Online Deep Learning for CSIT and CSIR Estimation of FDD Multi-User Massive MIMO Systems

3. Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges

4. MIMO Detector Selection with Federated Learning

5. Machine and Deep Learning for Resource Allocation in Multi-Access Edge Computing: A Survey

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