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Self-Sustaining Multiple Access with Continual Deep Reinforcement Learning for Dynamic Metaverse Applications

Year of publication

2023

Authors

Mazandarani, Hamidreza; Shokrnezhad, Masoud; Taleb, Tarik; Li, Richard

Organizations and authors

University of Oulu

Shokrnezhad Masoud Orcid -palvelun logo

Taleb Tarik Orcid -palvelun logo

Publication type

Publication format

Article

Parent publication type

Conference

Article type

Other article

Audience

Scientific

Peer-reviewed

Peer-Reviewed

MINEDU's publication type classification code

A4 Article in conference proceedings

Publication channel information

Parent publication name

Proceedings - 2023 IEEE International Conference on Metaverse Computing, Networking and Applications MetaCom 2023 Kyoto, Japan 26-28 June 2023

Conference

International Conference on Metaverse Computing, Networking and Applications

Publisher

IEEE

Pages

65-70

ISBN

979-8-3503-3333-6

​Publication forum

5475

​Publication forum level

1

Open access

Open access in the publisher’s service

No

Self-archived

Yes

License of the self-archived publication

Muu lisenssi

Other information

Fields of science

Electronic, automation and communications engineering, electronics

Keywords

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Publication country

United States

Internationality of the publisher

International

Language

English

International co-publication

Yes

Co-publication with a company

Yes

DOI

10.1109/MetaCom57706.2023.00024

The publication is included in the Ministry of Education and Culture’s Publication data collection

Yes