# Machine-Learning-Based Path Loss Prediction
## 1 Introduction  
Vehicle-to-vehicle (V2V) communication is crucial to intelligent transportation systems (ITS), particularly for increasing traffic efficiency and decreasing the probability of accidents [1]. Therefore, V2V investigations have great potential and are growing research trends in communication system design, implementation, and performance metrics. V2V propagation channels differ from cellular communication in the context of transceiver antennas, which are comparatively low in height and mobility, creating an environment that is both relatively complicated and rapidly changing [2]. In general, the power of wireless signals decreases with increasing distance between the transmitter and receiver, and electromagnetic waves propagate via a variety of methods, which are broadly categorized as reflection, diffraction, and scattering [3]. Estimating the received signal strength is challenging because of the complex V2V propagation environment. Path loss prediction is a crucial aspect of V2V communication systems, similar to wireless communication systems. Path loss generally refers to the attenuation of signal power as it propagates through space between two vehicles. This attenuation is influenced by factors such as distance, the presence of obstacles (e.g., buildings, vehicles), and the environment (urban vs. rural settings) [4]. Therefore, various path loss models have been developed for V2V communication channels under different traffic conditions, road types, and environments. Path loss models are commonly developed using deterministic or empirical methods. Most empirical models were created using data collected under specific conditions and within a particular frequency range [5]. These models provide a statistical description of the correlation between path loss and propagation factors such as frequency, antenna height, and distance between antennas [6; 7]. Furthermore, the reduction in signal strength induced by the shadowing effect is typically modeled as a Gaussian random variable with a mean of zero. Empirical models are characterized by simple model equations and minimal parameter requirements, which make them comparatively comprehensible. Nevertheless, when these models are implemented in more general situations, their precision may be reduced because their parameters are determined from the data gathered in specific environments [8]. Thus, these models may offer limited solutions for accurately predicting exactly what power is received at a particular location outside the environment in which they are developed. Instead, they can only offer a statistical overview of the path loss at a given distance. To reduce this limitation and obtain more generalizable results, many measurements and parameter estimations need to be made for different geographies and scenarios. This requires the use of new techniques that can be generalized and provide strong predictions based on existing data. In recent years, machine learning techniques have been suggested as powerful and robust tools for path loss prediction due to their main advantages, coming from their capacity to learn from data, their ability to generate more accurate models by training on different datasets, and quick adaptation to varying environments and conditions. In addition, higher prediction accuracy rates, especially in complex and dynamic systems, support its use in path loss predictions in V2V propagation environments [9]. Table 1 provides a comprehensive comparison of machine-learning-based path loss prediction studies in the literature.