Virtual Resource Allocation for Wireless Virtualized Heterogeneous Network with Hybrid Energy Supply

Zheng Chang, Tao Chen

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In this work, two novel virtual user association and resource allocation algorithms are introduced for a wireless virtualized heterogeneous network with hybrid energy supply. In the considered system, macro base stations (MBSs) are supplied by the grid power and small base stations (SBSs) have the energy harvesting capability in addition to the grid power supplement. Multiple infrastructure providers (InPs) own the physical resources, i.e., BSs and radio resources. The Mobile Virtual Network Operators (MVNOs) are able to recent these resources from the InPs and operate the virtualized resources for providing services to different users. In particular, aiming to maximize the overall utility for the MVNOs, a joint resource (spectrum and power) allocation and user association problem is presented. First, we present an alternating direction method of multipliers (ADMM)-based algorithm solution to find the near-optimal solution in a static manner. Moreover, we also utilize deep reinforcement learning to design the optimal policy without knowing a priori knowledge of the dynamic nature of networks. We have conducted extensive simulation and the performance evaluation demonstrate the advantages and effectiveness of the proposed schemes.

Original languageEnglish
JournalIEEE Transactions on Wireless Communications
DOIs
Publication statusAccepted/In press - 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • ADMM
  • deep learning
  • energy harvesting
  • Hybrid power systems
  • III-V semiconductor materials
  • Indium phosphide
  • Optimization
  • reinforcement learning
  • resource allocation
  • Resource management
  • Virtualization
  • wireless network virtualization
  • Wireless networks

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