###### Abstract
DL is a technique that uses the hierarchy of ANN to simulate ML activity during data processing in decision making, the human brain, and the nodal structuresof the nervous system. In the traditional method, the program analyzes the information linearly, while the functionality of a DL system allows the devices to process the data using a nonlinear method [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. V2X technology enables information exchange between moving elements of the transportation network that can affect vehicles and the main components of V2X, such as V2V and V2I communication technologies, etc. The main benefits of the V2X network, include improved road safety, energy efficiency, transportation efficiency, and management of the road. V2X technology allows vehicles to communicate over a WANET using the IEEE 802.11p standard. WANET has several classes such as MANET, VANETs, FANETs, etc. The requirements of ad hoc networks are becoming more reliable and valuable for all their possible applications. However, the network requires establishing a mechanism to detect and mitigate security issues, as they are crucial in the network, and their failure leads to undesirable results. Wireless networks are typically vulnerable to interference caused by outliers or noisy data [17]. The presence of outliers or noise causes problems in the transport network as they affect the network's performance and lead to unsafe and unreliable connections. In recent research, various techniques have been used to reduce the impact of outliers in wireless networks. Among these techniques, the algorithms of ML are the most promising method that can be improved by reducing the effects of outliers in vehicular communications. The ML model can be adapted to the new network model depending on the data collection. Many researchers have been proposed robust learning algorithms to overcome the possibility of outliers in training data; these algorithms are insensitive to outliers and can detect unseen observations. However, in implementing the robust algorithms, ML requires close monitoring and the necessary measures to narrow down the scope, understanding, and threat model [18; 19]. In many fields, such as industrial modeling, multilayer feedforward neural networks (MFNNs) or deep neural networks (DNNs) are used as approximators of nonlinear functions to provide advanced solutions for various applications. For training DNNs, a standard backpropagation algorithm based on minimizing the traditional MSE loss function is usually used. Unfortunately, this algorithm is prone and sensitive to outliers that distort the training data. As a result, the training data is contaminated with outliers, making the resulting model less reliable and potentially generating incorrect models, resulting in unacceptable performance. Outliers are observations that deviate significantly from other values in a random sample of mass information. Examples of these outliers include the information population with gross errors caused by critical measurement errors, incorrect decimals, transcription errors, accidental scaling of a member from another population, rounding errors, grouping errors, and hypothetical deviation. This article is about the performance estimation of V2X using DNNs. A set of robust statistical estimators, called M-estimators, have been proposed as robust loss functions to substitute the conventional MSE loss function, improve the training process, and robustize DL at the occurrence outliers in training data. The robustness in the presence of many outliers in V2X traffic datasets was illustrated in this work--finally, a comparative study with traditional and robust neural networks in terms of RMSE and MAPE. The motivations behind this study include:  
* Optimize quality of service (QoS) requirements and network monitoring to manage resources and ensure security. * Monitor network availability and activity to identify and eliminate outliers (anomalies), including security and operational issues.