ShanghaiTech University Knowledge Management System
| Machine Learning for Large-Scale Optimization in 6G Wireless Networks | |
| 2023 | |
| Source Publication | IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
 |
| ISSN | 2373-745X |
| EISSN | 1553-877X |
| Volume | PPIssue:99Pages:1-1 |
| Status | 已发表 |
| DOI | 10.1109/COMST.2023.3300664 |
| Abstract | The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from connected things to connected intelligence, featured by ultra high density, large-scale, dynamic heterogeneity, diversified functional requirements, and machine learning capabilities, which leads to a growing need for highly efficient intelligent algorithms. The classic optimization-based algorithms usually require highly precise mathematical model of data links and suffer from poor performance with high computational cost in realistic 6G applications. Based on domain knowledge (e.g., optimization models and theoretical tools), machine learning (ML) stands out as a promising and viable methodology for many complex large-scale optimization problems in 6G, due to its superior performance, computational efficiency, scalability, and generalizability. In this paper, we systematically review the most representative learning to optimize" techniques in diverse domains of 6G wireless networks by identifying the inherent feature of the underlying optimization problem and investigating the specifically designed ML frameworks from the perspective of optimization. In particular, we will cover algorithm unrolling, learning to branch-and-bound, graph neural network for structured optimization, deep reinforcement learning for stochastic optimization, end-to-end learning for semantic optimization, as well as wireless federated learning for distributed optimization, which are capable of addressing challenging large-scale problems arising from a variety of crucial wireless applications. Through the in-depth discussion, we shed light on the excellent performance of ML-based optimization algorithms with respect to the classical methods, and provide insightful guidance to develop advanced ML techniques in 6G networks. Neural network design, theoretical tools of different ML methods, implementation issues, as well as challenges and future research directions are also discussed to support the practical use of the ML model in 6G wireless networks. IEEE |
| Keyword | 5G mobile communication systems Computational efficiency Convex optimization Learning algorithms Reinforcement learning Semantics Wireless networks 6g 6g mobile communication Large-scale network Large-scale optimization Learning to optimize Machine learning algorithms Machine-learning Matching pursuit algorithms Mobile communications Nonconvex optimization Optimisations Wireless communications |
| URL | 查看原文 |
| Indexed By | EI |
| Language | 英语 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| EI Accession Number | 20233214504441 |
| EI Keywords | Deep neural networks |
| EI Classification Number | 461.4 Ergonomics and Human Factors Engineering ; 716.3 Radio Systems and Equipment ; 722.3 Data Communication, Equipment and Techniques ; 723.4 Artificial Intelligence ; 723.4.2 Machine Learning |
| Original Document Type | Article in Press |
| Source Data | IEEE |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/319444 |
| Collection | 信息科学与技术学院 信息科学与技术学院_PI研究组_石远明组 信息科学与技术学院_PI研究组_周勇组 信息科学与技术学院_博士生 信息科学与技术学院_PI研究组_廉黎祥组 |
| Affiliation | 1.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 6.School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW, Australia |
| Recommended Citation GB/T 7714 | Yandong Shi,Lixiang Lian,Yuanming Shi,et al. Machine Learning for Large-Scale Optimization in 6G Wireless Networks[J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS,2023,PP(99):1-1. |
| APA | Yandong Shi.,Lixiang Lian.,Yuanming Shi.,Zixin Wang.,Yong Zhou.,...&Wei Zhang.(2023).Machine Learning for Large-Scale Optimization in 6G Wireless Networks.IEEE COMMUNICATIONS SURVEYS AND TUTORIALS,PP(99),1-1. |
| MLA | Yandong Shi,et al."Machine Learning for Large-Scale Optimization in 6G Wireless Networks".IEEE COMMUNICATIONS SURVEYS AND TUTORIALS PP.99(2023):1-1. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
Edit Comment
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.