User-Oriented Edge Node Grouping in Mobile Edge Computing

Qing Li, Xiao Ma, Ao Zhou, Xiapu Luo, Shangguang Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


In mobile edge computing networks, densely deployed access points are empowered with computation and storage capacities. This brings benefits of enlarged edge capacity, ultra-low latency, and reduced backhaul congestion. This paper concerns edge node grouping in mobile edge computing, where multiple edge nodes serve one end user cooperatively to enhance user experience. Most existing studies focus on centralized schemes that have to collect global information and thus induce high overhead. Although some recent studies propose efficient decentralized schemes, most of them did not consider the system uncertainty from both the wireless environment and other users. To tackle the aforementioned problems, we first formulate the edge node grouping problem as a game that is proved to be an exact potential game with a unique Nash equilibrium. Then, we propose a novel decentralized learning-based edge node grouping algorithm, which guides users to make decisions by learning from historical feedback. Furthermore, we investigate two extended scenarios by generalizing our computation model and communication model, respectively. We further prove that our algorithms converge to the Nash equilibrium with upper-bounded learning loss. Simulation results show that our mechanisms can achieve up to 96.99% of the oracle benchmark.
Original languageEnglish
Pages (from-to)3691 - 3705
Number of pages15
JournalIEEE Transactions on Mobile Computing
Issue number6
Publication statusPublished - 1 Jun 2023


  • Mobile edge computing
  • edge node grouping
  • game theory
  • reinforcement learning

ASJC Scopus subject areas

  • Software
  • Electrical and Electronic Engineering
  • Computer Networks and Communications


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