| Popis: |
As wireless communication technology progresses towards the sixth generation (6G), high-frequency millimeter-wave (mmWave) communication has emerged as a promising candidate for enabling vehicular networks. It offers high data rates and low-latency communication. However, obstacles such as buildings, trees, and other vehicles can cause signal attenuation and blockage, leading to communication failures that can result in fatal accidents or traffic congestion. Predicting blockages is crucial for ensuring reliable and efficient communications. Furthermore, the advent of 6G technology is anticipated to integrate advanced sensing capabilities, utilizing a variety of sensor types. These sensors, ranging from traditional RF sensors to cameras and Lidar sensors, are expected to provide access to rich multimodal data, thereby enriching communication systems with a wealth of additional contextual information. Leveraging this multimodal data becomes essential for making precise network management decisions, including the crucial task of blockage detection. In this paper, we propose a Deep Learning (DL)-based approach that combines Convolutional Neural Networks (CNNs) and customized Vision Transformers (ViTs) to effectively extract essential information from multimodal data and predict blockages in vehicular networks. We train and evaluate our proposed method on the DL dataset framework for vision-aided wireless communications (ViWi) and demonstrate its potential for predicting blockages in vehicular networks through simulations. The results show that the proposed approach achieves over 95% accurate predictions, proving its potential for integration into 6G vehicular networks to enhance communication reliability and support advanced applications such as autonomous driving and smart city infrastructure. These findings underscore the practical significance and future impact of our work in advancing ultra-reliable and low-latency communication systems. |
