A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Adapting to Dynamic LEO-B5G Systems : Meta-Critic Learning Based Efficient Resource Scheduling (2022)

Yuan, Y., Lei, L., Vu, T. X., Chang, Z., Chatzinotas, S., & Sun, S. (2022). Adapting to Dynamic LEO-B5G Systems : Meta-Critic Learning Based Efficient Resource Scheduling. IEEE Transactions on Wireless Communications, 21(11), 9582-9595. https://doi.org/10.1109/TWC.2022.3178171

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Yuan, Yaxiong; Lei, Lei; Vu, Thang X.; Chang, Zheng; Chatzinotas, Symeon; Sun, Sumei

Lehti tai sarja: IEEE Transactions on Wireless Communications

ISSN: 1536-1276

eISSN: 1558-2248

Julkaisuvuosi: 2022

Ilmestymispäivä: 08.06.2022

Volyymi: 21

Lehden numero: 11

Artikkelin sivunumerot: 9582-9595

Kustantaja: Institute of Electrical and Electronics Engineers (IEEE)

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1109/TWC.2022.3178171

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/82559

Tiivistelmä

Low earth orbit (LEO) satellite-assisted communications have been considered as one of the key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose an exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve a massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments. We first propose an iterative suboptimal algorithm to provide an offline benchmark. To adapt to unforeseen variations, we propose an enhanced meta-critic learning algorithm (EMCL), where a hybrid neural network for parameterization and the Wolpertinger policy for action mapping are designed in EMCL. The results demonstrate EMCL’s effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods.

YSO-asiasanat: 5G-tekniikka; langattomat verkot; langaton tiedonsiirto; tietoliikennesatelliitit; resursointi; koneoppiminen; algoritmit

Vapaat asiasanat: LEO satellites; resource scheduling; reinforcement learning; meta-critic learning; dynamic environment

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2022

JUFO-taso: 3