A Conditional Generative Adversarial Network Based Approach for Network Slicing in Heterogeneous Vehicular Networks
Farnoush Falahatraftar,
Samuel Pierre,
Steven Chamberland
Affiliations
Farnoush Falahatraftar
Mobile Computing and Networking Research Laboratory (LARIM), Department of Computer and Software Engineering, Polytechnique de Montréal, Montreal, QC H3T 1J4, Canada
Samuel Pierre
Mobile Computing and Networking Research Laboratory (LARIM), Department of Computer and Software Engineering, Polytechnique de Montréal, Montreal, QC H3T 1J4, Canada
Steven Chamberland
Mobile Computing and Networking Research Laboratory (LARIM), Department of Computer and Software Engineering, Polytechnique de Montréal, Montreal, QC H3T 1J4, Canada
Heterogeneous Vehicular Network (HetVNET) is a highly dynamic type of network that changes very quickly. Regarding this feature of HetVNETs and the emerging notion of network slicing in 5G technology, we propose a hybrid intelligent Software-Defined Network (SDN) and Network Functions Virtualization (NFV) based architecture. In this paper, we apply Conditional Generative Adversarial Network (CGAN) to augment the information of successful network scenarios that are related to network congestion and dynamicity. The results show that the proposed CGAN can be trained in order to generate valuable data. The generated data are similar to the real data and they can be used in blueprints of HetVNET slices.
