6G Vision-Intelligent Millimeter-Wave Systems on High-Altitude Platform Stations

Broadband high-altitude platform station (HAPS) concepts have garnered notable attention from 6 G researchers due to their superior line-of-sight (LoS) probabilities, making them ideal for millimeter-wave (mmWave) and higher RF carrier frequencies. However, beam management processes for mmWave and higher carrier frequencies introduce substantial overhead. This paper analyzes vision-assisted HAPS 6G wireless communications using mmWave signaling. Machine Learning (ML) methods generate vision-based contextual information to facilitate mmWave beam selection, significantly reducing the need for extensive channel state information. The paper also analyzes a framework for vision-assisted millimeter-wave beam management that leverages visual information obtained by longrange vision sensors at the HAPS-gNB. Deep reinforcement learning methods, a highly efficient solution, provide intelligent assistance for managing 6G HAPS communications. The 6G Vision-Intelligent (6G-VI) ML system analysis utilizes a YOLOv8 deep learning model to identify the locations of User Equipment (UEs), which are then used as context for a millimeter-wave (mmWave) beam selection algorithm. The paper analyzes the 6G-VI method compared to other ML-based models, including Cascade Mask R-CNN, Mask R-CNN, Faster R-CNN, RetinaNet, and non-vision models. Simulation results indicate a minimum spectral efficiency improvement of $5 \mathrm{bits} / \mathrm{sec} / \mathrm{Hz}$ compared to other prior aerial HAPS systems.