Optimizing Flying Base Station Connectivity by RAN Slicing and Reinforcement Learning

Dick Carrillo Melgarejo, Jiri Pokorny, Pavel Seda, Arun Narayanan, Pedro H.J. Nardelli, Mehdi Rasti, Jiri Hosek, Milos Seda, Demostenes Z. Rodriguez, Yevgeni Koucheryavy, Gustavo Fraidenraich

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
10 Downloads (Pure)


The application of flying base stations (FBS) in wireless communication is becoming a key enabler to improve cellular wireless connectivity. Following this tendency, this research work aims to enhance the spectral efficiency of FBSs using the radio access network (RAN) slicing framework; this optimization considers that FBSs' location was already defined previously. This framework splits the physical radio resources into three RAN slices. These RAN slices schedule resources by optimizing individual slice spectral efficiency by using a deep reinforcement learning approach. The simulation indicates that the proposed framework generally outperforms the spectral efficiency of the network that only considers the heuristic predefined FBS location, although the gains are not always significant in some specific cases. Finally, spectral efficiency is analyzed for each RAN slice resource and evaluated in terms of service-level agreement (SLA) to indicate the performance of the framework.

Original languageEnglish
Pages (from-to)53746-53760
Number of pages15
JournalIEEE Access
Publication statusPublished - 2022
Publication typeA1 Journal article-refereed


  • deep-reinforcement learning
  • Flying base stations
  • location optimization
  • UAVs
  • wireless communication

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


Dive into the research topics of 'Optimizing Flying Base Station Connectivity by RAN Slicing and Reinforcement Learning'. Together they form a unique fingerprint.

Cite this