3D Positioning via Green Learning in mmWave Hybrid Beamforming Systems

Three-dimensional (3D) positioning technology plays an important role in millimeter wave (mmWave) non-terrestrial or integrated sensing and communication (ISAC) networks in sixth-generation (6G) systems. However, the complexity of joint range and orientation estimation in mmWave hybrid beamforming (HBF) systems forms a technical hurdle to its practical realization. In view of the recent advancement in green learning (GL) technology, a low-complexity GL architecture is developed herein for 3D positioning in mmWave HBF systems. The entire architecture only consists of one layer of unsupervised representation learning, followed by a supervised feature learning stage and a regression layer for parameter estimation. Compared to the typical deep learning method, the complexity of the proposed method is at least 3 order lower, while the performance is comparable to that of the maximum likelihood estimations of individual parameters given perfect knowledge of the other parameters. This presents the potential of GL in ISAC for future
$6G$
systems.