# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 1 Introduction  
Intelligent Transport Systems (ITS) play a significant role in improving road safety and optimizing traffic management. They rely on advanced wireless technologies to share a large amount of data collected from hundreds of embedded sensors. These information exchanges are referred to as Vehicle-to-Everything (V2X) communications, and they encompass all the communications between a vehicle and its environment [1]. Two major wireless technologies have emerged to ensure this connectivity. On one hand, the ITS-G5 has been developed by the European Telecommunications Standards Institute (ETSI), based on the IEEE 802.11p access layer. It represents an extension of the general WiFi standard optimized for vehicular environments [2]. On the other hand, the Cellular Vehicle-to-Everything (C-V2X) communications have been introduced by the Third Generation Partnership Project (3 GPP) with release 14 of the Long-Term Evolution (LTE) standard [3], then expanded in release 16 with the coming of the 5G New-Radio (NR) [4]. The coexistence of ITS-G5 and C-V2X technologies will satisfy the specific requirements of transport services in terms of latency, reliability and coverage. However, several challenges will arise, since they both operate over the 5.9 GHz spectrum. One solution to facilitate this coexistence and avoid interference consists of detecting and identifying the wireless technology, then dynamically selecting the appropriate transmission channel. Therefore, the efficiency of the spectrum usage relies on the ability of the ITS station to accurately identify the received signal [5]. Signal identification has been an intensive topic of research over the last two decades. In the context of cognitive radio, the classification of digitally modulated signals has been performed in several studies [6, 7, 8, 9, 10]. The authors of [7, 8] exploit the statistics derivedfrom the instantaneous features of the incoming signals, whereas the algorithms proposed in [9; 10] are based, respectively, on the statistical moments and cumulants of these signals. Another study, conducted in [11], aims to recognize single carrier modulated signals versus Orthogonal Frequency Division Multiplexing (OFDM) signals based on their cyclostationarity. This property has aroused a great deal of interest in the research community and has been employed for the identification of some standard signals as well, such as the Global System for Mobile communication (GSM) versus LTE in [12], and Worldwide Interoperability for Microwave Access (WiMAX) versus LTE in [13]. All the above-mentioned methods belong to the feature-based statistical approach. It consists of extracting explicit features from the received signal, then passing them through a classification algorithm where the decision is made based on their observed values [14]. This decision making step is mostly based on the analysis of the probability distribution function of the feature vectors or the evaluation of the Euclidean distance between their prescribed and estimated values. They have both been proven to be simple to implement, with near-optimal performance. However, they are sensitive to the noise level and/or require a priori information on the received signal [6]. Moreover, the manually set decision parameters, such as thresholds, make it challenging to systematically adapt these techniques whenever a new wireless technology emerges. Recently, deep learning techniques have been rapidly developed and have made great strides in the signal identification field. For example, convolutional neural networks can be said to be the most popular architecture for both modulation and wireless technology recognition [15; 16]. Although this approach performs well in different applications and has the advantage of simple feature pre-processing or even raw data usage, it requires large-scale training data resulting in high implementation costs [17]. Moreover, the availability of datasets for wireless communications is one of the biggest challenges for researchers.