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.

This is the second part of a two-part paper that focuses on link-adaptation (LA) and physical layer (PHY) abstraction for multi-user MIMO (MU-MIMO) systems with non-linear receivers. The first part proposes a new metric, called bit-metric decoding rate (BMDR) for a detector, as being the equivalent of post-equalization signal-to-interference-noise ratio (SINR) for non-linear receivers. Since this BMDR does not have a closed form expression, a machine-learning based approach to estimate it effectively is presented. In this part, the concepts developed in the first part are utilized to develop novel algorithms for LA, dynamic detector selection from a list of available detectors, and PHY abstraction in MU-MIMO systems with arbitrary receivers. Extensive simulation results that substantiate the efficacy of the proposed algorithms are presented.