From Empirical Measurements to Augmented Data Rates: A Machine Learning Approach for MCS Adaptation in Sidelink Communication

TL;DR

This paper introduces a machine learning method to predict optimal MCS levels for V2X sidelink communication, improving data rates without feedback channels, supported by a new extensive real-world dataset.

Contribution

It presents a novel ML-based approach for MCS adaptation in V2X sidelink, including quantile prediction, and provides a publicly available dataset for future research.

Findings

Significant improvements over traditional MCS selection methods

Effective use of quantile prediction for MCS level estimation

Public dataset from extensive drive tests for research use

Abstract

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.