Hierarchical Reinforcement Learning for Multi-Layer Multi-Service Non-Terrestrial Vehicular Edge Computing-Reference-Cited by-同舟云学术

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Hierarchical Reinforcement Learning for Multi-Layer Multi-Service Non-Terrestrial Vehicular Edge Computing

Published:2024 Issue: Volume:2 Page:1045-1061
ISSN:2831-316X
Container-title:IEEE Transactions on Machine Learning in Communications and Networking
language:
Short-container-title:Trans. Mach. Learn. Comm. Netw.

Author:

Shinde Swapnil Sadashiv¹^ORCID,Tarchi Daniele²^ORCID

Affiliation:

1. Consorzio Nazionale Interuniversitario delle Telecomunicazioni (CNIT), University of Bologna Research Unit, Bologna, Italy

2. Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi,”, University of Bologna, Bologna, Italy

Funder

European Union

European Commission

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/9882533/10356147/10609447.pdf?arnumber=10609447

Reference34 articles.

1. Comprehensive Survey on Machine Learning in Vehicular Network: Technology, Applications and Challenges

2. Towards a Novel Air–Ground Intelligent Platform for Vehicular Networks: Technologies, Scenarios, and Challenges

3. Mobile Edge Computing in Unmanned Aerial Vehicle Networks

4. Non-Terrestrial Networks in the 6G Era: Challenges and Opportunities

5. UAV/HAP-Assisted Vehicular Edge Computing in 6G: Where and What to Offload?

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