Multi-Controller Resource Management for Software-Defined Wireless Networks

Feixiang Li, Xiaobin Xu, Haipeng Yao, Jingjing Wang, Chunxiao Jiang, Song Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


Given decoupling the control layer and the infrastructure layer, the software-defined wireless networks (SDWNs) is beneficial in terms of providing both low-latency and low-energy consumption services for mobile users, where multi-controller placement and resource management become a pair of bottlenecks. In this letter, we propose an energy-aware multi-controller placement scheme as well as a latency-aware resource management model for the SDWN. Moreover, the particle swarm optimization is invoked for solving the multi-controller placement problem, and a deep reinforcement learning algorithm-aided resource allocation strategy is conceived. Finally, experimental results show that our proposed schemes are conducive to reducing both the execution time and the energy consumption of each task.

Original languageEnglish
Article number8606120
Pages (from-to)506-509
Number of pages4
JournalIEEE Communications Letters
Issue number3
Publication statusPublished - 1 Mar 2019


  • deep reinforcement learning
  • Multi-controller placement
  • particle swarm algorithm
  • resource management

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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