Machine Learning Empowered Emerging Wireless Networks in 6G: Recent Advancements, Challenges and Future Trends-Reference-Cited by-同舟云学术

Machine Learning Empowered Emerging Wireless Networks in 6G: Recent Advancements, Challenges and Future Trends

Published:2023 Issue: Volume:11 Page:83017-83051
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Noman Hafiz Muhammad Fahad¹^ORCID,Hanafi Effariza¹^ORCID,Noordin Kamarul Ariffin¹^ORCID,Dimyati Kaharudin¹^ORCID,Hindia Mhd Nour¹,Abdrabou Atef²^ORCID,Qamar Faizan³^ORCID

Affiliation:

1. Department of Electrical Engineering, Advanced Communication Research and Innovation (ACRI), Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

2. Department of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, Al Ain, United Arab Emirates

3. Centre for Cyber Security, Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia

Funder

United Arab Emirates University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10209158.pdf?arnumber=10209158

Reference166 articles.

1. AI-Empowered Trajectory Anomaly Detection and Classification in 6G-V2X

2. Federated Reinforcement Learning-Based Resource Allocation for D2D-Aided Digital Twin Edge Networks in 6G Industrial IoT

3. Edge on Wheels With OMNIBUS Networking for 6G Technology

4. 6G Cellular Networks and Connected Autonomous Vehicles

5. DRL-Based Sum-Rate Maximization in D2D Communication Underlaid Uplink Cellular Networks

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