FeDRL-D2D: Federated Deep Reinforcement Learning- Empowered Resource Allocation Scheme for Energy Efficiency Maximization in D2D-Assisted 6G Networks-Reference-Cited by-同舟云学术

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FeDRL-D2D: Federated Deep Reinforcement Learning- Empowered Resource Allocation Scheme for Energy Efficiency Maximization in D2D-Assisted 6G Networks

Published:2024 Issue: Volume:12 Page:109775-109792
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Muhammad Fahad Noman Hafiz¹^ORCID,Dimyati Kaharudin¹^ORCID,Ariffin Noordin Kamarul¹^ORCID,Hanafi Effariza¹^ORCID,Abdrabou Atef²^ORCID

Affiliation:

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

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

Funder

Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10613769.pdf?arnumber=10613769

Reference55 articles.

1. Integration of Hybrid Networks, AI, Ultra Massive-MIMO, THz Frequency, and FBMC Modulation Toward 6G Requirements: A Review

2. Federated learning for green and sustainable 6G IIoT applications

3. Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

4. System-Wide Energy Efficient Computation Offloading in Vehicular Edge Computing With Speed Adjustment

5. Power Allocation for NOMA With Cache-Aided D2D Communication

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