The cellular industry's shift from long-distance radio signals to short-distance millimeter waves is one of the 5G era's biggest changes, expected to continue with submillimeter waves over the next decade. To more precisely direct millimeter wave and future terahertz-frequency signals toward user devices, Arizona State University researchers have developed ViWi-BT, a vision-wireless framework that improves beam tracking using computer vision and deep learning. Smartphones historically operated much like other long-distance radios, scanning the airwaves for omnidirectional tower signals and tuning into whatever was strongest and/or closest. But in the 5G and 6G eras, networks of small cells will use beamforming antennas to more specifically target their signals in a given direction toward discovered client devices, which may be contemplating connections from multiple base stations at once. ViWi-BT's goal is to use AI and a device's cameras or lidar capabilities to identify physical impediments and advantages for the beam targeting process, enabling "vision-aided wireless communications."