###### Abstract  
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. vehicular network; CGAN; congestion in vehicular network; SDN; network slicing 2021  
[PERSON], [PERSON], [PERSON], [PERSON] 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](https://doi.org/10.3390/telecom2010009)]([https://doi.org/10.3390/telecom2010009](https://doi.org/10.3390/telecom2010009))  
16 March 2021  
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**Vol. 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). **  
**Vol. 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons. are mainly based on the computation of the available and the needed resources. For example, the authors in [9; 10; 11] considered the Channel Busy Ratio (CBR) to find congestion in vehicular networks using the European Telecommunications Standards Institute (ETSI) standardization. Finding an optimal value for CBR that prevents communication channel from under-utilization is a serious challenge in these works. Whenever the amount of required network resources is higher than the amount of available resources, the network management system could find out that the smooth data flow may disappear and congestion could even occur. In highly dynamic network systems, like Heterogeneous Vehicular Networks (HetVNETs), the network's topology, the number of users, the type of required services, and the amount of required resources are changing quickly and dynamically. Therefore, following constant and predefined policies for the current situation is not a promising way to provide reliable services. Because network conditions could change rapidly, applying prior generated policies and solutions is not applicable to network problems. In the light of this issue, it is worth giving dynamicity to the proposed solutions. In other words, if we have a very dynamic network, like HetVNET, then it may be a novel idea to propose a method that can provide network resources and requirements quickly and dynamically based on network templates. These templates can be used to dynamically create the HetVNET slices. Integrating Artificial Intelligence (AI) with SDN based architecture is a promising potential solution to the challenges of the dynamicity of heterogeneous networks [12]. To the best of our knowledge, in the current scientific works that are related to vehicular networks, there is a lack of an intelligent SDN-NFV based architecture that could provide network templates in HetVNET environment using computing power of fog objects.