## 2 Relevant Works  
Many researchers focus on improving V2X communication performance using ML techniques to significantly improve traffic safety, energy efficiency, and traffic efficiency in establishing reliable communication. One of the primary approaches to obtain a robust learning algorithm that is more robust to outliers is to replace the loss function of the performance measure MSE with another robust function to enhance the training process and robustize DL in the presence of outliers. In this approach, robustness against outliers is satisfied by minimizing the effect of significant training errors due to outliers.  
Moreover, there have been some previous efforts to develop robust learning algorithms to improve performance in the presence of outliers. Moreover, many researchers have focused on estimating and detecting outliers in wireless networks using ML techniques. In this work, we studied the performance of V2X traffic using DNNs based on M-estimator loss functions as a replacement for the standard MSE loss when the data contains outliers.  
[PERSON] et al. [20] presented robust ANN learning algorithms that enabled robust statistical methods, called M-estimators, as a robust loss function to substitute the conventional MSE loss function for studying VANET traffic performance. The research [21] addressed the estimation of energy for VANET performance using robust NN training algorithms and proposed robust M-estimator loss functions for the case of noise-free information. [PERSON] et al. [22] investigated the applicability of machine learning to address problems in highly mobile vehicular communication and prompting ML to manage the resultant difficulties in vehicular networks. The research [23] presented a set of robust, statistical M-estimators as a replacement for the conventional MSE loss function using good, noise-free information. Furthermore, new transfer functions that depend on robust statistical m-estimators proposed alternatives to traditional transfer functions using a dataset containing outliers [24].  
The DL algorithm has addressed detecting an anomaly in 5G networks regarding network latency [25]. In [26], the robustness of different ANNs training algorithms has been investigated using the robust M-estimators as a loss function in order to robustize learning in the presence of outliers. [PERSON] et al. [27] studied DL's performance for abnormality discovery in 5G networks. [PERSON] et al. [28] examined anomaly detection by DNN in tracking IoT traffic for future smart-city applications.  
[PERSON] et al. [29] proposed an approach based on a robust estimation technique called M-estimator as a robust loss function to improve the robustness of the ANN training procedure. [PERSON] [30] introduced a novel robust performance measure depending on SoftMax Loss for DNN training in the presence of noisy data and showed its robustness for different sets of contaminated data. [PERSON] et al. [31] presented robust NN classifiers using a novel robust loss function known as M-estimator in the presence of corrupted data. [PERSON] et al. [32] combined the advantages of the assumed \(\tau\)-estimates of the Nonlinear Regression Model (NRM) and the Backpropagation Algorithm depending on M-estimators as a robust loss function, in order to create the TAO robust learning algorithm that handles the problems of model fitting with outliers.  
Moreover, [PERSON] et al., 2021 and [PERSON] et al., 2020 have presented 5G and 6G technology components for V2X communication [33; 34]. In particular, in [33], the authors discussed the evolution from the existing cellular V2X technology to New Radio-V2X, focusing on the main features and functions of the physical layer, sidelink communication, and its resource allocation, precise positioning techniques, security, privacy mechanisms, and architecture flexibility. On the other hand, several critical technologies from different areas, such as new tools, algorithms, and system structures, are presented [34].