A1 Journal article (refereed)
Virtual Resource Allocation for Wireless Virtualized Heterogeneous Network with Hybrid Energy Supply (2022)

Chang, Z., & Chen, T. (2022). Virtual Resource Allocation for Wireless Virtualized Heterogeneous Network with Hybrid Energy Supply. IEEE Transactions on Wireless Communications, 21(3), 1886-1896. https://doi.org/10.1109/twc.2021.3107867

JYU authors or editors

Publication details

All authors or editors: Chang, Zheng; Chen, Tao

Journal or series: IEEE Transactions on Wireless Communications

ISSN: 1536-1276

eISSN: 1558-2248

Publication year: 2022

Volume: 21

Issue number: 3

Pages range: 1886-1896

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/twc.2021.3107867

Publication open access: Not open

Publication channel open access: Channel is not openly available

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/85532

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.

Keywords: wireless networks; virtualisation; energy harvesting; resourcing; machine learning; deep learning

Free keywords: energy harvesting; ADMM; reinforcement learning; deep learning; wireless network virtualization; resource allocation

Contributing organizations

Ministry reporting: Yes

VIRTA submission year: 2022

JUFO rating: 3