# Deep Learning for Predicting Traffic in V2X Networks
## 2 Literature Review  
Several studies have addressed the prediction of traffic in 5G mobile networks using ML. In this work, we focus on predicting V2X traffic using the DL method based on the LSTM model. Therefore, in this section, we provide an overview of previous studies related to our main topic. [PERSON] [13] investigated the prediction accuracy of the 5G cellular network and developed a smoothed LSTM traffic forecasting method. The model adjusts the number of hidden layers and units under the adaptive prediction accuracy approach to minimize the randomness of the 5G traffic sequence, simultaneously. [PERSON] et al. [14] comprehensively overviewed the current ML methods for predicting cellular traffic information in 5G networks. [PERSON] et al. [15] addressed cellular traffic and performed traffic prediction using an LSTM model with recurrent neural networks (RNNs). The information about mobile traffic is obtained from the physical downlink control channel (PDCCH). They evaluated the proposed method's single-step and long-term prediction errors considering different periods with observed values. [PERSON] et al. [16] developed a strategy to incorporate analytical time series into the 5G core and predict risks that could lead to system failures. [PERSON] et al. [17] used a DL method built on an LSTM network to implement a time series prediction of energy consumption for drone-based MEC. Four cases were examined to determine the accuracy based on the training's learning rate. They assessed the prediction accuracy using RMSE and MAPE as measures of prediction accuracy to identify the best prediction accuracy and the maximum average improvement in prediction accuracy. [PERSON] et al. [18] presented an intelligent base station sleep system (BS) to reduce system power consumption while guaranteeing the best QoE. In addition, they introduced an LSTM learning approach for predicting the traffic allocation in the service area to determine when to trigger the BS sleep process; in addition, they created a successful three-stepprocess to choose which of the BSs should be awakened or put to sleep. In [19], traffic prediction was performed using DL and an LSTM network to make predictions about traffic activity in IoT-enabled edge computing. [PERSON] et al. [20] presented a spatiotemporal analysis of mobile network traffic and gave an overview of current research in this area. They proposed a graph-attention network based on time series similarity. Network traffic prediction (NTP) based on a DL using an LSTM network was investigated in [21; 22]. [PERSON] et al. [23] addressed the short-term forecasting of 5G traffic flow using edge computing for efficient smart city services. A new framework for predicting traffic flows based on the LSTM network, focusing mainly on these irregular traffic flows, has been proposed in [24]. The authors of [25] investigate the validity and feasibility of RNNs, and in particular, LSTMs as potential tools for such predictions. Thus, a new method of QoS prediction is presented that uses an LSTM model and network-based QoS metrics to identify patterns and predict QoS for connected and automated vehicles (CAV) that will soon be available. [PERSON] et al. [26] suggested and analyzed a new resource demand prediction strategy using an ML algorithm. The support vector machine (SVM), time-delay neural network (TDNN), and LSTM were investigated and compared to determine the optimal prediction method. [PERSON] et al. [27] investigated a prediction of base station mobility to extend service in 5G networks. A DL using an LSTM network with adjusted hyperparameters is proposed to predict the short-term traffic speed on a parallel multilane arterial road in a growing country such as Vietnam [28]. For cellular traffic prediction, [PERSON] et al. [29] proposed a model combining a single-exponential smoothing LSTM. The single-exponential smoothing method was used to fit the volume due to the complexity and diversity of network traffic forms.