Abstract

Recent advances in Vehicle-to-Everything (V2X) technology and the upcoming sixth-generation (6G) network will dawn a new era for vehicular services with enhanced communication capabilities. Connected and Autonomous Vehicles (CAVs) are expected to deliver a new transportation experience, increasing the safety and efficiency of road networks. The use of millimeter-wave (mmW) frequencies guarantees a huge amount of bandwidth ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 1GHz) and a high data rate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 10 Gbit/s), which are required for CAVs applications. However, high frequency is impaired by severe path loss, and line of sight (LoS) propagation can be easily blocked by static and dynamic obstacles. Several solutions are being investigated, and the most promising one exploits relays. However, traditional relay schemes react to link failure and leverage instantaneous information, which impedes efficient relay selection in highly mobile and complex networks, such as vehicular scenarios. In this context, we propose a novel proactive relaying strategy that exploits the cooperation between CAVs and environment information to predict the dynamic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LoS-map</i> , which describes the links' evolution in time. The proactive relaying schemes exploit the dynamic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LoS-map</i> to maximize the network connectivity. A novel framework integrating realistic mobility patterns and geometric channel propagation models is proposed to analyze the performance in different scenarios. Numerical simulations suggest that a novel proactive relaying strategy that exploits the cooperation between CAVs and environment information to predict the dynamic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LoS-map</i> , which describes the links' evolution in time. .

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