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

Shi, Yandong; Lian, Lixiang; Shi, Yuanming; Wang, Zixin; Zhou, Yong; Fu, Liqun; Bai, Lin; Zhang, Jun; Zhang, Wei

doi:10.1109/comst.2023.3300664

articleIEEE Communications Surveys & TutorialsJan 1, 2023Closed access

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

YSYandong Shi LLLixiang Lian YSYuanming Shi ZWZixin Wang YZYong Zhou

ShanghaiTech University · China Telecom (China) · +7 more institutions

Indexed incrossref

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…

Citation impact

183

total citations

FWCI: 22.77
Percentile: 100%
References: 282

Citations per year

Authors

9

Topics & keywords

Topics

Keywords

Computer science
Scale (ratio)
Wireless network
Wireless
Artificial intelligence
Machine learning
Telecommunications
Geography

No related works found for this paper.