# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 2 Related Work  
The past decade has seen significant advancements in the realm of vehicular communications, with researchers exploring various facets of signal propagation, channel estimation, and path loss prediction, especially in urban vehicular environments. A prominent study by [PERSON] and [PERSON] presented a digital-twin-enabled deep learning approach, which combined a digital representation of the real-world system with deep learning, outperforming existing methodologies in dynamic urban vehicular environments [11]. Similarly, a deep learning-based channel estimation methodology for Vehicle-to-Everything (V2X) communications has been proposed, addressing the rapid time variations and non-stationary characteristics of wireless channels [12]. [PERSON] and [PERSON] demonstrated the practical constraints of translating physical-layer network coding theories integrated with deep neural networks into real-world applications for vehicular ad-hoc networks [13]. Path loss prediction has been another area of focus. [PERSON] et al. [14] introduced a comprehensive exploration of machine-learning-driven path loss prediction, highlighting the superior performance of machine-learning models compared to traditional models. [PERSON] et al. [15] further advanced this by synergizing three pivotal techniques, ANN, Gaussian process, and PCA, for feature selection, presenting a holistic machine-learning framework for path loss modeling. [PERSON] [16] explored the challenges of signal propagation in indoor environments, especially within the realm of 5G, proposing a data-driven approach leveraging artificial intelligence. Vehicular Ad-Hoc Networks (VANETs) have also been extensively researched. [PERSON], [PERSON], and [PERSON] focused on the transmission of basic safety messages in VANETs, proposing a reinforcement learning-based congestion control approach [17]. [PERSON] and [PERSON] emphasized the potential of vehicular visible light communication as a V2X communication method, introducing a machine-learning framework for improved path loss modeling [18]. Moreover, [PERSON] et al. [19] proposed a non-parametric, data-driven approach using the Random Forest learning method for V2V path loss prediction. Signal strength prediction is also a critical domain. [PERSON] et al. [20] introduced a methodology harnessing the power of ANNs to compute received signal strength based on atmospheric parameters. [PERSON] et al. [21] further explored this by leveraging satellite imagery with deep learning for signal strength prediction. [PERSON] and [PERSON] presented an analytical framework for estimating the reliability of V2V communications within cognitive radio-VANETs, integrating a Markov model and an ANN model for streamlined reliability estimation [22]. Although these studies have made significant advancements within their respective domains, our present work distinguishes itself by focusing on signal degradation in urban VANETs. Our approach employs supervised learning anchored in ANNs to estimate signal degradation based on obstacles encountered by moving vehicles. Table 1 concisely summarizes these studies based on their focus areas, primary methodologies, and applicability, particularly within the urban VANET context: