A1 Journal article (refereed)
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 authors or editors


Publication details

All authors or editorsYuan, Yaxiong; Lei, Lei; Vu, Thang X.; Chang, Zheng; Chatzinotas, Symeon; Sun, Sumei

Journal or seriesIEEE Transactions on Wireless Communications

ISSN1536-1276

eISSN1558-2248

Publication year2022

Publication date08/06/2022

Volume21

Issue number11

Pages range9582-9595

PublisherInstitute of Electrical and Electronics Engineers (IEEE)

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1109/TWC.2022.3178171

Publication open accessOpenly available

Publication channel open accessPartially open access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/82559


Abstract

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.


Keywords5G technologywireless networkswireless data transmissioncommunications satellitesresourcingmachine learningalgorithms

Free keywordsLEO satellites; resource scheduling; reinforcement learning; meta-critic learning; dynamic environment


Contributing organizations


Ministry reportingYes

Reporting Year2022

JUFO rating3


Last updated on 2024-03-04 at 18:06