论文标题
$π$ - 路：在V2X方案中的按需紧急切片的学习框架
$π$-ROAD: a Learn-as-You-Go Framework for On-Demand Emergency Slices in V2X Scenarios
论文作者
论文摘要
预计在不久的将来将成为5G业务的主要驱动因素之一。设想专用的\ emph {网络切片}，以满足高级V2X服务的严格要求，例如自动驾驶，旨在大大减少道路伤亡。但是，随着V2X服务变得越来越关键任务，还需要设计新的解决方案，以确保其成功提供服务，即使在特殊情况下，例如道路事故，拥塞等。在这种情况下，我们建议$π$ - 路，A \ emph {Deep Learning}框架，以自动学习沿道路的常规移动交通模式，检测非经常性事件并按严重性水平进行分类。 $π$ -ROAD使运营商可以根据需要实例\ emph {主动}实例化专用\ emph {紧急网络切片（ENS）}，同时根据其服务的关键性级别重新降低现有切片。我们的框架通过在欧洲高速公路$ 400〜 km $内收集的实际移动网络痕迹验证，并增强了有关相关道路活动的公开信息。我们的结果表明，$π$ - 公路成功地检测并归类了非重新播放的道路事件，并减少了$ 30 \％$ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $。
Vehicle-to-everything (V2X) is expected to become one of the main drivers of 5G business in the near future. Dedicated \emph{network slices} are envisioned to satisfy the stringent requirements of advanced V2X services, such as autonomous driving, aimed at drastically reducing road casualties. However, as V2X services become more mission-critical, new solutions need to be devised to guarantee their successful service delivery even in exceptional situations, e.g. road accidents, congestion, etc. In this context, we propose $π$-ROAD, a \emph{deep learning} framework to automatically learn regular mobile traffic patterns along roads, detect non-recurring events and classify them by severity level. $π$-ROAD enables operators to \emph{proactively} instantiate dedicated \emph{Emergency Network Slices (ENS)} as needed while re-dimensioning the existing slices according to their service criticality level. Our framework is validated by means of real mobile network traces collected within $400~km$ of a highway in Europe and augmented with publicly available information on related road events. Our results show that $π$-ROAD successfully detects and classifies non-recurring road events and reduces up to $30\%$ the impact of ENS on already running services.
