Optimizing Flying Base Station Connectivity by RAN Slicing and Reinforcement Learning

Optimizing Flying Base Station Connectivity by RAN Slicing and Reinforcement Learning

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 optimi...

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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Flying base stations
UAVs
location optimization
wireless communication
deep-reinforcement learning
Online Access:https://ieeexplore.ieee.org/document/9775679/

Internet

https://ieeexplore.ieee.org/document/9775679/

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