A Survey of Graph-Based Resource Management in Wireless Networks—Part II: Learning Approaches

This two-part survey provides a comprehensive review of graph optimization and learning for resource management in wireless networks. In Part I, we introduced the fundamentals of graph optimization and provided a recent literature review of graph optimization for resource management in various wireless communication scenarios. In this part, we first present an overview of graph learning and introduce several modern graph neural network models. Then, a state-of-the-art literature review of graph learning for different resource management issues in wireless networks is provided, which covers power control, spectrum management, beamforming design, task scheduling, and aerial coverage planning. Furthermore, we discuss current technical challenges and future research directions of graph optimization and learning for resource management in future wireless networks.

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Publication Details

Journal
IEEE Transactions on Cognitive Communications and Networking
Published
2024-11-29
DOI
https://doi.org/10.1109/tccn.2024.3508777
Citations
20
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
6.98

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article

A Survey of Graph-Based Resource Management in Wireless Networks—Part II: Learning Approaches

Shuguang Cui, Ling Lyu, Min Sheng, Xiucheng Wang et al.
20 citations
IEEE Transactions on Cognitive Communications and Networking
Distributed and Parallel Computing Systems
6.98
article

A Survey of Graph-Based Resource Management in Wireless Networks—Part II: Learning Approaches

Shuguang Cui, Ling Lyu, Min Sheng, Xiucheng Wang, Yanpeng Dai, Nan Cheng, Lin Cai, Junyu Liu, Xuemin Shen
article en
20 citations

Abstract

This two-part survey provides a comprehensive review of graph optimization and learning for resource management in wireless networks. In Part I, we introduced the fundamentals of graph optimization and provided a recent literature review of graph optimization for resource management in various wireless communication scenarios. In this part, we first present an overview of graph learning and introduce several modern graph neural network models. Then, a state-of-the-art literature review of graph learning for different resource management issues in wireless networks is provided, which covers power control, spectrum management, beamforming design, task scheduling, and aerial coverage planning. Furthermore, we discuss current technical challenges and future research directions of graph optimization and learning for resource management in future wireless networks.

IEEE Transactions on Cognitive Communications and NetworkingVol. 11(4)
Xidian University (CN), University of Waterloo (CA), University of Victoria (CA), Chinese University of Hong Kong, Shenzhen (CN), Xi’an University of Posts and Telecommunications (CN), Dalian Maritime University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation
Openalex Percentile: Top 3%
Distributed and Parallel Computing Systems
6.98
DOISourceOpenAlex
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A Survey of Graph-Based Resource Management in Wireless Networks—Part II: Learning Approaches — Shuguang Cui, Ling Lyu, et al. · IEEE Transactions on Cognitive Communications and Networking (2024) | TGRS Research Map | TGRS