论文标题
自动驾驶中车辆动态频谱访问的分布式学习
Distributed Learning for Vehicular Dynamic Spectrum Access in Autonomous Driving
论文作者
论文摘要
自主驾驶汽车之间的可靠无线通信是保证乘客安全和舒适的基本需求之一。但是，当通信汽车的数量增加时，由于使用频带的占用无线电无线电，传输质量可能会大大降低。在本文中，我们专注于自动驾驶平台用例，其中平台内的通信是在动态选择的频带中进行的，除了名义上专门用于此类目的之外。载体选择以灵活的方式在路边单元（无线通信基础架构的边缘）的上下文数据库的支持下进行。但是，由于数据库仅向白冈领导者提供上下文信息，因此最终决定是由单个排的，按照人工智能算法提出的建议。在这项工作中，我们专注于轻巧的Q学习解决方案，该解决方案可以在每个汽车中成功实现，以进行动态通道选择。
Reliable wireless communication between the autonomously driving cars is one of the fundamental needs for guaranteeing passenger safety and comfort. However, when the number of communicating cars increases, the transmission quality may be significantly degraded due to too high occupancy radio of the used frequency band. In this paper, we concentrate on the autonomous vehicle-platooning use-case, where intra-platoon communication is done in the dynamically selected frequency band, other than nominally devoted for such purposes. The carrier selection is done in a flexible manner with the support of the context database located at the roadside unit (edge of wireless communication infrastructure). However, as the database delivers only context information to the platoons' leaders, the final decision is made separately by the individual platoons, following the suggestions made by the artificial intelligence algorithms. In this work, we concentrate on a lightweight Q-learning solution, that could be successfully implemented in each car for dynamic channel selection.
