From Empirical Measurements to Augmented Data Rates: A Machine Learning
  Approach for MCS Adaptation in Sidelink Communication
Asif Abdullah Rokoni Daniel Schäufele Martin Kasparick Sławomir Stańczak
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.