A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Chang, Zheng; Chen, Tao

Lehti tai sarja: IEEE Transactions on Wireless Communications

ISSN: 1536-1276

eISSN: 1558-2248

Julkaisuvuosi: 2022

Volyymi: 21

Lehden numero: 3

Artikkelin sivunumerot: 1886-1896

Kustantaja: Institute of Electrical and Electronics Engineers (IEEE)

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: Julkaisukanava ei ole avoin

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/85532

Tiivistelmä

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.

YSO-asiasanat: langattomat verkot; virtualisointi; energian kerääminen; resursointi; koneoppiminen; syväoppiminen

Vapaat asiasanat: energy harvesting; ADMM; reinforcement learning; deep learning; wireless network virtualization; resource allocation

Liittyvät organisaatiot

OKM-raportointi: Kyllä

VIRTA-lähetysvuosi: 2022

JUFO-taso: 3