## 2 Related Work
Indeed, the proposed method is mainly based on tuning the resource allocation and transmission power in the network slices, without any future forecast in the network situation and applying AI methods. Based on their study, computational complexity is polynomial (cubic), which takes time to converge. This indicates that the stochastic solutions may not be appropriate for such a dynamic and fast-changing vehicular network, unless they are applied to strong computational devices, such as fog devices in vehicular networks. In [21], Signal to Interference plus Noise Ratio (SINR) has been measured for every vehicle in the predefined infotainment and autonomous network slices. Subsequently, the vehicles with higher amount of SINR provide streaming video services for vehicles with lower amount of SINR. The obtained results show an improvement in the throughput of infotainment slices and reduction in packet reception ratio for safety applications of autonomous slices. In, [22], the authors proposed a network slicing framework for Internet of Vehicle (IoV) based on multiple types of Radio Access Technologies (RATs) and traffics, using cloud computing. The results obtained from real deployment showed the scalability of the proposed framework. However, the application of AI methods to provide a flexible predicted resource allocation mechanism could significantly improve the results. This issue has been considered in [23, 24]; however, the vehicular network is not the targeted network in these studies. In [23], the authors proposed the dynamic resource allocation technique using deep reinforcement learning. The proposed method showed better performance when compared to the other techniques, such as heuristic and random approaches. However, based on the results, slicing performance is negatively affected by an increase in the network load. In [24], deep reinforcement learning was been applied to allocate resources in the network slices. In [25], the authors showed that the network slicing could benefit from the proposed traffic prediction method using AI. In [26], the authors proposed a method for network resources allocation with respect to the traffic load in vehicular networks. A Monte Carlo tree search utilizing cross entropy with new metric was proposed by the authors. Although the proposed method is not based on prior network information, it shows good performance in simulation scenarios with a large of fog devices and network slices. However, in simulation scenarios with a low number of fog devices and network slices, the performance of the proposed method was not as good as any other comparable method while using Q-learning. Besides, the Mont Carlo tree search requires a lot of memory and it is slow to perform, which is evident in the results of the operation time. Therefore, the proposed method is not a worthy choice to be applied in a dynamic vehicular network that changes fast. Moreover, it might be better to use more intelligent methods that could be based on analyzing previous network experiences and information than the Mont Carlo tree search method. In [27], the authors proposed a method for defining clusters of vehicles and vehicle slices of network. The clusters are used to exchange safety related messages via Vehicle-to-Vehicle (V2V) communications. The network slices are used to transmit video streaming data via Vehicle-to-Road Side Unit. From the results, the proposed method performs better in the scenario with a low number of vehicles. This indicates that, with the increasing number of vehicles and crowded urban roads, there is still a problem with the transmission of high data loads, even using the proposed method. Based on the literature, there are two groups of proposed approaches for network slicing in vehicular networks: non-intelligent methods and intelligent methods. In this section, we explained some of the drawbacks of some studied non-intelligent works. The proposed intelligent methods are mainly based on the machine learning and deep learning methods.