Adaptive Beam Tracking in RIS-Assisted Mobile Communication Systems Using Active Sensing and Deep Learning

This paper proposes a deep learning framework that leverages recurrent neural networks and graph neural networks to adaptively design the RIS sensing vectors and reflection coefficients, as well as the AP beamformers, in order to maintain reliable communications with multiple mobile user equipments.