High-Efficient Resource Allocation Based on Deep Q-Network Enhanced Federated Learning in Slicing Packet Network for 5G ToB Service
We propose a computing power scheduling algorithm based on Deep Q-Network enhanced federated learning in Slicing Packet Network. Experimental demonstration and numerical evaluation show its overall feasibility and efficiency.
Authors
- Hui Yang (ORCID: https://orcid.org/0009-0005-2034-7735)
- Tiankuo Yu (ORCID: https://orcid.org/0000-0001-5279-1033)
- Sunling Gong (ORCID: https://orcid.org/0000-0003-3380-0859)
- Qiuyan Yao (ORCID: https://orcid.org/0000-0001-8753-6489)
- Jie Zhang (ORCID: https://orcid.org/0000-0001-5946-8319)
- Wenxin Liu (ORCID: https://orcid.org/0000-0001-7456-7606)
Institutions
- Beijing University of Posts and Telecommunications (CN)
Publication Details
- Published
- 2024-08-16
- DOI
- https://doi.org/10.1109/icait62580.2024.10808124
- Citations
- 1
- Primary Topic
- Software-Defined Networks and 5G
- Type
- article
- Field-Weighted Citation Impact
- 0.35