## 3 Methodology
### A Hybrid CGAN-SDN Architecture  
HetVNET is a dynamic and stochastic network with a rapidly changing number of users, network topology, and required services. The volume of generated data using safety and infotainment application varies very fast and it is difficult to predict. Moreover, most of the network communication features, such as signal strength, signal attenuation, and path link stability, are vulnerable from other parameters, such as temporary and permanent barriers, vehicles movements, speed and direction. As far as these features of HetVNET are concerned, it is mandatory to have a central intelligent management mechanism in order to meet the requirements of the network. This issue is comprehensively studied and proposed as an open challenge in [12]. Figure 4: The architecture of the proposed CGAN that shows generator’s layers and discriminator’s layers. Figure 3: CGAN model for HetVNET. Because the velocity of change in HetVNET and requirements is high and varies, using the same slices for a long time is risky; therefore, the ability of the slices to meet all of the network requirements is not guaranteed. Instead, we can generate network slices based on previous successful network experiences. It is like making new generation of HetVNET slices from a strong population. With this vision, on one hand, the risk of having slices with low performance is reduced and, on the other hand, it affects the performance of the entire HetVNET over time by creating a good generation of slices. When considering the literature related to network congestion in vehicular network, using WAVE and ETSI standards, the proposed congestion controlling mechanisms are mostly based on tuning the value of transmission power and data transmission rate [11, 28, 29, 30, 31, 32, 33]. Finding the optimal value for these parameters is not a promising solution, due to the high dynamicity of the vehicular network. In other words, there is no optimal fixed value for these parameters, instead, the appropriate value should be applied temporarily for these parameters during a predefined time interval and just based on the current network situation. These values are similar (not equal) to the values that were previously used and made successful network experiences. In this work, the synthetic data generated by the proposed CGAN are the data close (not equal) to the original ones that came from the successful scenarios. Therefore, we create the most similar data with little variance to the previous successful ones to apply in network slices. When considering the wide applications of Generative Adversarial Network (GAN) in producing new images, by our proposed approach, we can generate several images for each part (slice) of vehicular network (let us assume that, in this problem, images are the network various configurations in terms of the five variables). These generated configurations can be applied in various slices with different number of vehicles. Therefore, providing network slices with the appropriate value for the five parameters that are tailored to the current network situation in terms of the number of vehicles is proposed in this work. The proposed CGAN method can quickly generate a volume of information useful in creating and setting many HetVNET slices with respect to the network service needs. When considering the five variables of \(v\), \(dr\), \(tp_{DSRC}\), \(tp_{LTE}\), and \(b\) and their possible real values, in each time, the number of possible templates for the slices can be calculated by multiplying the number of possible values that each of these parameters could have. Subsequently, these templates can be categorized based on the number of vehicles. With the NFV, the necessary and common network functions and services can be deployed and installed in the virtual servers. The Virtual Network Functions (VNFs) are the software functions, which provide network services and functionality virtually.