毫米波车到基础设施（V2I）通信中跨视图视觉辅助的主动基站选择与波束预测

中文摘要 AI 辅助

本文研究环境感知辅助的毫米波（mmWave）车到基础设施（V2I）无线系统的主动基站（BS）选择与波束预测。我们利用车载全景街景图像和预加载的卫星地图，预测车辆周围与通信相关的环境信息，包括附近建筑的 footprint（ footprint 指建筑占地轮廓）和高度。预测的高度图提供了紧凑的环境先验信息，与历史移动性信息结合，共同预测下一时隙的视距（LoS）状态、传输速率以及发射和接收波束选择。在覆盖澳大利亚新南威尔士州不同真实区域的数据集上，所提框架实现了91.4%的视距分类准确率、0.638 bps/Hz的传输速率预测平均绝对误差，在地理上未见过的区域中，传输速率比传统反应式基线高出40%以上，优于所有评估的可部署基于学习的基线。该数据集和代码将在指定的https URL发布。

英文摘要

This paper investigates environmental-sensing-aided proactive base station (BS) selection and beam prediction for millimeter-wave (mmWave) vehicle-to-infrastructure (V2I) wireless systems. We exploit onboard panoramic street-view images and a preloaded satellite map to predict communication-relevant environmental information around the vehicle, including nearby building footprints and heights. The predicted height map provides a compact environmental prior and is combined with historical mobility information to jointly predict the next-slot line-of-sight (LoS) state, transmission rate, and transmit and receive beam selections. On our dataset covering different real-world regions across New South Wales, Australia, the proposed framework achieves 91.4% LoS classification accuracy, 0.638 bps/Hz mean absolute error of data rate prediction, and more than 40% higher transmission rate than the conventional reactive baseline in geographically unseen regions, outperforming all evaluated deployable learning-based baselines. The dataset and code will be released at https://github.com/Huzijiao/Cross-view_V2I

发表机构

• University of Sydney（悉尼大学）
• Australian National University（澳大利亚国立大学）
• National and Kapodistrian University of Athens（雅典国家卡波蒂斯坦大学）

机构由 AI 辅助整理，请以论文原文为准。