论文标题
智能运输系统的编排：经验教训和潜在机会
Intelligent Transportation Systems' Orchestration: Lessons Learned & Potential Opportunities
论文作者
论文摘要
全球5G网络的部署工作不断增长，导致了企业/服务数字化转型的加速。这种增长导致需要新的交流技术，以促进这种转变。正在提出6G作为将实现该目标的技术和架构集。在5G网络中出现的主要用例中，将继续在6G网络中发挥关键作用是智能运输系统（ITS）。凭借开发和部署高效和有效的ITS的所有预计好处，需要解决一系列独特的挑战。一个突出的挑战是其编排，因为各种支持技术和用于提供所需应用程序/服务的异质网络。为此，本文通过强调文献中相关的先前作品并列出了从当前的部署编排工作中汲取的经验教训，从而详细介绍了其编排挑战。它还提供了多种潜在的数据驱动的研究机会，可以部署诸如增强学习和联合学习之类的范式，以提供有效有效的其编排。
The growing deployment efforts of 5G networks globally has led to the acceleration of the businesses/services' digital transformation. This growth has led to the need for new communication technologies that will promote this transformation. 6G is being proposed as the set of technologies and architectures that will achieve this target. Among the main use cases that have emerged for 5G networks and will continue to play a pivotal role in 6G networks is that of Intelligent Transportation Systems (ITSs). With all the projected benefits of developing and deploying efficient and effective ITSs comes a group of unique challenges that need to be addressed. One prominent challenge is ITS orchestration due to the various supporting technologies and heterogeneous networks used to offer the desired ITS applications/services. To that end, this paper focuses on the ITS orchestration challenge in detail by highlighting the related previous works from the literature and listing the lessons learned from current ITS deployment orchestration efforts. It also presents multiple potential data-driven research opportunities in which paradigms such as reinforcement learning and federated learning can be deployed to offer effective and efficient ITS orchestration.
