Effective and Efficient Beam Tracking with Green Learning

This work proposes a novel machine learning (ML) based beam tracking scheme. The proposed scheme is inspired by a new learning method, called Green Learning (GL), which has several nice properties including lightweight, low complexity, and logical transparency. GL has demonstrated outstanding performance in the field of image/video processing, and this work continues showing its great potentials in beam tracking problem. In a setting of 5G NR (New Radio) beam tracking protocol, the proposed GL beam tracking scheme demonstrates several advantages over conventional ML methods including an improved accuracy in tracking angle of arrival, less sensitive to channel signal-to-noise ratio, an extended time duration to lose tracking, and lower computational complexity in both training and inference. These effective and efficient properties make the proposed GL beam tracking suitable for future communications, especially with critical demand of low carbon footprint.