A Conditional Generative Adversarial Network Based Approach for Network Slicing in Heterogeneous Vehicular Networks
Mobile Computing and Networking Research Laboratory (LARIM), Department of Computer and Software Engineering, Polytechnique de Montréal, Montreal, QC H3T 1J4, Canada
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Telecom 2021, 2(1), 141-154; https://doi.org/10.3390/telecom2010009
Received: 16 January 2021
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Revised: 11 March 2021
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Accepted: 15 March 2021
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Published: 18 March 2021
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.
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Keywords:
vehicular network; CGAN; congestion in vehicular network; SDN; network slicing
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MDPI and ACS Style
Falahatraftar, F.; Pierre, S.; Chamberland, S. A Conditional Generative Adversarial Network Based Approach for Network Slicing in Heterogeneous Vehicular Networks. Telecom 2021, 2, 141-154. https://doi.org/10.3390/telecom2010009
AMA Style
Falahatraftar F, Pierre S, Chamberland S. A Conditional Generative Adversarial Network Based Approach for Network Slicing in Heterogeneous Vehicular Networks. Telecom. 2021; 2(1):141-154. https://doi.org/10.3390/telecom2010009
Chicago/Turabian StyleFalahatraftar, Farnoush, Samuel Pierre, and Steven Chamberland. 2021. "A Conditional Generative Adversarial Network Based Approach for Network Slicing in Heterogeneous Vehicular Networks" Telecom 2, no. 1: 141-154. https://doi.org/10.3390/telecom2010009
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