Advanced Learning Schemes for Metaverse Applications in B5G/6G Networks

Sherief Hashima, Mostafa M. Fouda, Kohei Hatano, Eiji Takimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The metaverse, a virtual world where various users can jointly/independently interact with digital objects seamlessly, is expected to be a key application area in beyond fifth generation (B5G)/ six-generation (6G) networks. However, the metaverse design poses several challenges, such as efficient usage of limited resources and the provision of high-quality user experiences. In this paper, we recommend advanced machine learning techniques such as multi-armed bandits (MABs), federated learning (FL), meta-learning, etc., to address these challenges. We illustrate the evaluability of these techniques with two specific use cases: resource allocation for virtual reality (VR) applications and personalized content delivery in the metaverse.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages799-804
Number of pages6
ISBN (Electronic)9798350333336
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023 - Kyoto, Japan
Duration: Jun 26 2023Jun 28 2023

Publication series

NameProceedings - 2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

Conference

Conference2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023
Country/TerritoryJapan
CityKyoto
Period6/26/236/28/23

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Media Technology
  • Computer Networks and Communications

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