Collision prediction is crucial for safety in Vehicle-to-Everything (V2X) networks, essential for smart cities and intelligent transportation. Traditional methods struggle with complexity, while learning-based approaches face challenges in feature extraction and prediction accuracy. To address this, we propose attentive TabNet for collision prediction in V2X networks. Our model utilizes attention mechanisms in deep neural networks to focus on key spatial and temporal features, enhancing prediction reliability. We also apply data preprocessing to handle imbalances. Experimental evaluations using real-world V2X data show that our method outperforms random forest and neural network-based algorithms in accuracy and efficiency. Both simulations and real-world tests confirm its effectiveness. By improving collision prediction, our approach enhances road safety, advancing intelligent transportation and smart city initiatives.

In this work, we rigorously investigate the robustness of graph neural fractional-order differential equation (FDE) models. This framework extends beyond traditional graph neural (integer-order) ordinary differential equation (ODE) models by implementing the time-fractional Caputo derivative. Utilizing fractional calculus allows our model to consider long-term memory during the feature updating process, diverging from the memoryless Markovian updates seen in traditional graph neural ODE models. The superiority of graph neural FDE models over graph neural ODE models has been established in environments free from attacks or perturbations. While traditional graph neural ODE models have been verified to possess a degree of stability and resilience in the presence of adversarial attacks in existing literature, the robustness of graph neural FDE models, especially under adversarial conditions, remains largely unexplored. This paper undertakes a detailed assessment of the robustness of graph neural FDE models. We establish a theoretical foundation outlining the robustness characteristics of graph neural FDE models, highlighting that they maintain more stringent output perturbation bounds in the face of input and graph topology disturbances, compared to their integer-order counterparts. Our empirical evaluations further confirm the enhanced robustness of graph neural FDE models, highlighting their potential in adversarially robust applications.

In this paper, we propose a deep learning (DL) based receiver named comm-transformer network (Comm-Trans Net), which is robust for different sub-types of tapped delay line (TDL) channels. The novel Comm-Trans Net considers the attention mechanism to compensate for the multi-path fading effect for different orthogonal frequency-division multiplexing (OFDM) subcarriers. We propose a novel positional encoding method for each OFDM subcarrier, which uses the attention mechanism to offset the deep fading effect. In particular, our proposed Comm-Trans Net serves as an integrated DL-based receiver for unknown channel conditions. Our results show that the proposed Comm-Trans Net can outperform the bit-error rate (BER) performance compared to minimum mean-square error (MMSE) channel estimation with generalized approximate message passing (GAMP) receiver, and also outperforms the state-of-the-art DL-based receivers. Moreover, our proposed Comm-Trans Net is robust for multiple communication scenarios ranging from the TDL-A channel to the TDL-E channel with different levels of non-line-of-sight (NLoS) and line-of-sight (LoS) components. Also, the computational complexity of the proposed Comm-Trans Net is studied, which shows that the attention mechanism for channel positional encoding is a cost-effective solution to improve the quality of service.