## 1 Introduction
In addition, [4] showed enhancement in the network's average end-to-end delay and PDR by adopting V2V communication to identify the nearest vehicle to communicate with, which significantly reduced travel time by communicating the emergency message received instantly to all nearby vehicles on the route. To acquire different road traffic parameters, they applied an optimization technique. Finally, they used various simulation scenarios and assumptions to evaluate and examine the suggested traffic management strategy. Recently, several applications have adopted deep learning as a powerful tool to create models that can be applied on devices in real life by learning from given parameters. The deep learning model can then produce a prediction according to the learned relation between parameters. Furthermore, the model can be re-trained from time to time to update its weights due to changes occurring in the environment and affecting the parameters. Among the diverse techniques of deep learning, several applications such as biomedical [9], electrical [10], and IoT [11] have applied the one-dimensional convolutional neural network (1D-CNN) and proved its effectiveness. A distributed deep learning (DDL) framework was developed by [12] to improve distributed non-convex constrained optimizations in wireless networks. To efficiently assign states based on local inspections, where multiple computing nodes interconnected by backhaul links, they used an infinite-capacity backhaul to facilitate nodes to communicate in a lossless way and a practical finite-capacity backhaul that uses distributed solvers supplied by quantizers to communicate over the capacity-limited backhaul. They also deployed deep neural networks (DNNs) to precisely approximate the solution's unknown computation, which results from the distributed nature and the non-convexity of the optimizations. To avoid V2X communication bottlenecks, [13] examined a dual optimization problem regarding resource allocation and transmission mode selection over C-V2X communications. To encounter the V2V pair's reliability and latency demands and expand the sum capacity of V2I users as well, they implemented a deep reinforcement learning (DRL)-based decentralized algorithm. A security-aware approach for enhancing SE and EE based on deep reinforcement learning (SEED) to improve spectrum efficiency (SE) and energy efficiency (EE) over a platooning network was presented by [14]. SEED deployed deep Q network (DQN) to convert the optimization problem to the transmission power and spectrum selections of V2X links. Simulation results showed that the efficiency of SEED is better than the DQN-wopa algorithm by 31.83% and better than the baseline algorithm by 68.40%. Moreover, in [15], to improve the resource allocation issue for mixed distributed/centralized V2X communication systems under various network loading conditions, the authors introduced an approach using resource allocation mode selection and power control. They examined UE autonomous resource selection mode (distributed system) and scheduled resource allocation (centralized system) and attained a reliable matching between VUEs and their reuse PUE partners. By developing a hopping strategy, which is based on the lowest propagation loss and considering the environment's effect Morocho-Cayamcela, [16] were able to enhance the reachability of multi-hop communications and environmental recognition. In 2021 to develop the performance of congestion control in CV2X sidelink, [17] implemented a DCC algorithm using DRL, which permitted the DRL agent to observe a CBR state to choose the packet transmission rate that boosts the reward of packet delivery rate (PDR) while preserving superior channel utilization. An interference avoidance DDL model for IoT and device to any destination communication was presented by [11] to improve EE and overall system throughput. Regarding root mean squared error and mean absolute error, their model outperformed the state-of-the-art regression benchmarks.