Beam tracking using deep learning applied to 6G MIMO

Abstract

This work explores the application of machine learning to enhance beam tracking in 6G MIMO Vehicle-to-Infrastructure (V2I) communications. Beam tracking, essential for sustaining reliable mmWave connections, remains challenging due to the high mobility of vehicular environments and the significant overhead associated with millimeter wave MIMO beamforming. While beam selection has been extensively studied, ML-based beam tracking is relatively underexplored, largely due to the scarcity of comprehensive datasets. To bridge this gap, this study introduces a novel public multimodal dataset, designed in accordance with 3GPP requirements, which combines wireless channel data with multimodal sensor information. This dataset supports the evaluation of advanced data fusion algorithms specifically tailored to V2I scenarios. Furthermore, a custom recurrent neural network (RNN) architecture is proposed as a robust solution for effective beam tracking, leveraging temporal and multimodal data to address the challenges of dynamic vehicular communications.