## 2 Background and Related Work
We observe that the VANET exchanges messages and establishes a communication link for Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and roadside network infrastructure for Vehicle-to-Roadside (V2R) communications. Figure 2 illustrates various VANET applications. A number of applications have already been proposed which are likely to be designed for next-generation vehicles, including safety monitoring, map localization, parking localization, distance warning, V2V communication, vehicle collision warning, cooperative intersection safety, internet access, and driver assistance [11]. VANETs utilize short-range communication based on IEEE 802.11 wireless technology and rely on geographic positions for information exchange between network nodes. GPS receivers in vehicles enable node positions but challenges such as intermittent availability and signal loss remain. Intelligent Digital Twin-based Software-Defined Vehicular Networks (IDT-SDVN) are an improvement to the Software-Defined Vehicular Network (SDVN) architecture highlighted in [12]. Its purpose is to overcome the constraints that are commonly associated with routing policies in vehicular networks. The methodology suggested, called ELITE routing, comprises the following four stages: policy training, policy generation, policy deployment, and relay selection. By utilizing Digital Twins, this system generates virtual instances of physical objects and constructs virtual network spaces, thereby facilitating the development of complex routing policies. The paper referenced above presents simulation results that indicate that ELITE routing provides significant enhancements in packet delivery ratio, end-to-end latency, and communication overhead when compared to currently employed techniques. However, specific information regarding the extent of these enhancements and the pragmatic factors to be considered when implementing this methodology is absent  
Figure 1: A typical vehicular ad hoc network (VANET) scenario. Figure 2: VANET applications [11]. from the paper. Additional investigation might be required to authenticate the practical feasibility of IDT-SDVN and ELITE routing. An extension of the above study is presented in [13], in which the authors presented IGNITE, a solution to address the issues in Vehicle Edge Computing (VEC) by leveraging clustering algorithms and Deep Reinforcement Learning (DRL). The system enhances the process of determining when to offload tasks in the constantly changing network structure of VEC, while also incorporating a mechanism for receiving and responding to feedback. Although the study shows potential, it is intricate, and the practical use and scalability of this method have not been explored. The inclusion of an analysis of the real-world viability and practicability of the paper's findings, together with recommendations for future research work, would enhance its overall value. A novel deep learning-driven methodology for the selection of analog beams in millimeter-wave (mm Wave) was investigated in [14]. By using wide beam measurements and two codebooks with varying beam widths, this scheme estimates and predicts the received powers of narrow beams. The problem is conceptualized as a super-resolution task, in which super-resolution techniques are used to derive high-resolution beam images from low-resolution beam images. Furthermore, spatial, and temporal correlations between beam qualities are incorporated into the method used to approach optimal beam selection performance. The simulation outcomes provide evidence that the suggested method for beam selection substantially mitigates the expenses linked to beam training, all the while preserving optimal performance. Another related study on enhancing the efficacy of beamforming at the base station (BS) was conducted in [15]. This study introduced a composite beam and power allocation strategy for multiuser millimeter-wave (mm Wave) networks that is guided by deep learning (DL).