From Empirical Measurements to Augmented Data Rates: A Machine Learning Approach for MCS Adaptation in Sidelink Communication
This work addresses the problem of adaptive MCS selection for higher data rates in vehicle-to-everything communication, though it is incremental as it builds on existing machine learning techniques for a specific domain.
The paper tackles the challenge of selecting modulation and coding schemes (MCS) in C-V2X sidelink communication without a feedback channel, proposing a machine learning approach with quantile prediction that shows significant improvements over conventional methods, and it introduces a publicly available real-world dataset from drive tests to address data scarcity.
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.