Reinforcement Learning-Based Multi-Domain Network Slice Provisioning
Publication Date
1-1-2023
Document Type
Conference Proceeding
Publication Title
IEEE International Conference on Communications
Volume
2023-May
DOI
10.1109/ICC45041.2023.10278745
First Page
1899
Last Page
1904
Abstract
We address the problem of establishing an end-to-end network slice across multiple domains and propose a Reinforcement Learning-based framework that enables multiple domains to collaborate on end-to-end network slicing admission and allocation. The objective is to maximize the long-term revenue of the network operator. We employ a Graph Neural Network (GNN) to capture the topology features as the encoder. The simulation results show that our framework improves the profit of the network operator by up to 15% compared to a greedy algorithm.
Funding Number
CNS-2008856
Funding Sponsor
National Science Foundation
Keywords
Graph Neural Network, Machine Learning, Network Slice, Reinforcement Learning, Resource Allocation
Department
Computer Science
Recommended Citation
Zhouxiang Wu, Genya Ishigaki, Riti Gour, Congzhou Li, Feng Mi, Subhash Talluri, and Jason P. Jue. "Reinforcement Learning-Based Multi-Domain Network Slice Provisioning" IEEE International Conference on Communications (2023): 1899-1904. https://doi.org/10.1109/ICC45041.2023.10278745
