A Survey of Graph-Based Resource Management in Wireless Networks—Part I: Optimization Approaches

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

Abstract: The evolution of wireless communications and networking technologies has led significantly expansion of the dimensionality of network resources, which compels innovations in resource management. Graphs, a classic discrete mathematical tool, have long been widely used for resource management thanks to their capabilities to model complex relationships and interactions among elements in wireless networks. Recently, resource management over graphs embraces various advanced approaches of graph optimization and graph learning, aligned with evolving demands in future wireless networks. To better learn recent research landscape and explore important trends, this two-part survey provides a comprehensive overview for resource management via graph optimization and learning. Part I presents the fundamentals of graph optimization and provides a recent literature review of graph optimization for resource management in various wireless communication scenarios, including cellular networks, device-to-device communications, multi-hop networks, multi-antenna systems, edge caching and computing, and non-terrestrial networks. Part II gives the basics of graph learning and provides a state-of-the-art literature review of graph learning in wireless networks for addressing various resource management issues, covering power control, spectrum management, beamforming design, task scheduling, and aerial coverage planning. A discussion of technical challenges and future research directions is covered in Part II.

External IDs:doi:10.1109/tccn.2024.3508783