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 editors: Yuan, Yaxiong; Lei, Lei; Vu, Thang X.; Chang, Zheng; Chatzinotas, Symeon; Sun, Sumei

Journal or series: IEEE Transactions on Wireless Communications

ISSN: 1536-1276

eISSN: 1558-2248

Publication year: 2022

Publication date: 08/06/2022

Volume: 21

Issue number: 11

Pages range: 9582-9595

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication country: United States

Publication language: English

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

Publication open access: Openly available

Publication channel open access: Partially 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.

Keywords: 5G technology; wireless networks; wireless data transmission; communications satellites; resource allocation; machine learning; algorithms

Free keywords: LEO satellites; resource scheduling; reinforcement learning; meta-critic learning; dynamic environment

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

Preliminary JUFO rating: 3