Due to the lack of a feedback channel in the C-V2X sidelink, finding a
suitable modulation and coding scheme (MCS) is a difficult task. However,
recent use cases for vehicle-to-everything (V2X) communication with higher
demands on data rate necessitate choosing the MCS adaptively. In this paper, we
propose a machine learning approach to predict suitable MCS levels.
Additionally, we propose the use of quantile prediction and evaluate it in
combination with different algorithms for the task of predicting the MCS level
with the highest achievable data rate. Thereby, we show significant
improvements over conventional methods of choosing the MCS level. Using a
machine learning approach, however, requires larger real-world data sets than
are currently publicly available for research. For this reason, this paper
presents a data set that was acquired in extensive drive tests, and that we
make publicly available.

本文中我们提出了一种使用机器学习方法预测适当调制编码方案 (MCS) 等级的方法，并结合不同算法对具有最高数据传输率的 MCS 等级进行定位预测，并在此基础上，显示出相较于传统方法选择 MCS 等级的显著改进。然而，使用机器学习方法需要比目前公开的研究数据集更大的真实世界数据集。因此，本文介绍了在广泛的行驶测试中获取的数据集，并将其公开可用。