AHCI RESEARCH GROUP

Publications

Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2025

Aloudat, M. Z.; Aboumadi, A.; Soliman, A.; Al-Mohammed, H. A.; Al-Ali, M.; Mahgoub, A.; Barhamgi, M.; Yaacoub, E.

Metaverse Unbound: A Survey on Synergistic Integration Between Semantic Communication, 6G, and Edge Learning Journal Article

In: IEEE Access, vol. 13, pp. 58302–58350, 2025, ISSN: 21693536 (ISSN).

Abstract | Links | BibTeX | Tags: 6g wireless system, 6G wireless systems, Augmented Reality, Block-chain, Blockchain, Blockchain technology, Digital Twin Technology, Edge learning, Extended reality (XR), Language Model, Large language model, large language models (LLMs), Metaverse, Metaverses, Semantic communication, Virtual environments, Wireless systems

@article{aloudat_metaverse_2025,

title = {Metaverse Unbound: A Survey on Synergistic Integration Between Semantic Communication, 6G, and Edge Learning},

author = {M. Z. Aloudat and A. Aboumadi and A. Soliman and H. A. Al-Mohammed and M. Al-Ali and A. Mahgoub and M. Barhamgi and E. Yaacoub},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003088610&doi=10.1109%2fACCESS.2025.3555753&partnerID=40&md5=8f3f9421ce2d6be57f8154a122ee192c},

doi = {10.1109/ACCESS.2025.3555753},

issn = {21693536 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Access},

volume = {13},

pages = {58302–58350},

abstract = {With a focus on edge learning, blockchain, sixth generation (6G) wireless systems, semantic communication, and large language models (LLMs), this survey paper examines the revolutionary integration of cutting-edge technologies within the metaverse. This thorough examination highlights the critical role these technologies play in improving realism and user engagement on three main levels: technical, virtual, and physical. While the virtual layer focuses on building immersive experiences, the physical layer highlights improvements to the user interface through augmented reality (AR) goggles and virtual reality (VR) headsets. Blockchain-powered technical layer enables safe, decentralized communication. The survey highlights how the metaverse has the potential to drastically change how people interact in society by exploring applications in a variety of fields, such as immersive education, remote work, and entertainment. Concerns about privacy, scalability, and interoperability are raised, highlighting the necessity of continued study to realize the full potential of the metaverse. For scholars looking to broaden the reach and significance of the metaverse in the digital age, this paper is a useful tool. © 2013 IEEE.},

keywords = {6g wireless system, 6G wireless systems, Augmented Reality, Block-chain, Blockchain, Blockchain technology, Digital Twin Technology, Edge learning, Extended reality (XR), Language Model, Large language model, large language models (LLMs), Metaverse, Metaverses, Semantic communication, Virtual environments, Wireless systems},

pubstate = {published},

tppubtype = {article}

}

2024

Ding, P.; Liu, J.; Sun, M.; Li, L.; Liu, H.

Enhancing Computational Processing Performance for Generative AI Large Models with Autonomous Decision-Making in Metaverse Applications Proceedings Article

In: Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom, pp. 253–258, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833151599-7 (ISBN).

Abstract | Links | BibTeX | Tags: Adversarial machine learning, AGI (Artificial General Intelligence), Artificial general intelligence, Artificial general intelligences, Autonomous decision, Autonomous Decision-Making, Data assimilation, Data integration, Decisions makings, Digital Twin Technology, Emotion Recognition, Generative adversarial networks, Generative AI large model, Generative AI Large Models, Large models, Metaverse, Metaverses, Model Acceleration, Model Compression, Multi agent systems, Multi-agent systems, Multi-modal data, Multi-Modal Data Integration, Multiagent systems (MASs), Reinforcement Learning, Reinforcement learnings, Spatio-temporal data

@inproceedings{ding_enhancing_2024,

title = {Enhancing Computational Processing Performance for Generative AI Large Models with Autonomous Decision-Making in Metaverse Applications},

author = {P. Ding and J. Liu and M. Sun and L. Li and H. Liu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211489063&doi=10.1109%2fMetaCom62920.2024.00048&partnerID=40&md5=ae085a7d90b12c9090f5bf7a274bc7ce},

doi = {10.1109/MetaCom62920.2024.00048},

isbn = {979-833151599-7 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom},

pages = {253–258},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {We explore how to enhance the computational processing performance for generative AI large models with autonomous decision-making in metaverse applications. We first introduce the relationship between AI large models and the Metaverse. We elaborate on the application scenarios of generative AI large models in Metaverse, including real-time weather simulation, embodied intelligence of agents, dynamic environment interaction, and user emotion recognition. We then propose the method of Multi-Dimensional Optimization Generation Framework (MDOGF) to improve computational processing performance. The experiment results show great improvement in computational processing performance. © 2024 IEEE.},

keywords = {Adversarial machine learning, AGI (Artificial General Intelligence), Artificial general intelligence, Artificial general intelligences, Autonomous decision, Autonomous Decision-Making, Data assimilation, Data integration, Decisions makings, Digital Twin Technology, Emotion Recognition, Generative adversarial networks, Generative AI large model, Generative AI Large Models, Large models, Metaverse, Metaverses, Model Acceleration, Model Compression, Multi agent systems, Multi-agent systems, Multi-modal data, Multi-Modal Data Integration, Multiagent systems (MASs), Reinforcement Learning, Reinforcement learnings, Spatio-temporal data},

pubstate = {published},

tppubtype = {inproceedings}

}