"FeDRL-D2D: Federated Deep Reinforcement Learning- Empowered Resource ..."

Hafiz Muhammad Fahad Noman et al. (2024)

> Home

Details and statistics

DOI: 10.1109/ACCESS.2024.3434619

access: open

type: Journal Article

metadata version: 2024-08-22

- view
  - electronic edition via DOI (open access)
  authority control:
- export record
  dblp key:
  - journals/access/NomanDNHA24
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/access/NomanDNHA24
Hafiz Muhammad Fahad Noman, Kaharudin Dimyati, Kamarul Ariffin Noordin, Effariza Hanafi, Atef Abdrabou:
FeDRL-D2D: Federated Deep Reinforcement Learning- Empowered Resource Allocation Scheme for Energy Efficiency Maximization in D2D-Assisted 6G Networks. IEEE Access 12: 109775-109792 (2024)

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.