Computer Vision-Aided Proactive Mobility Management for 6G Terahertz Communications

Recently, terahertz (THz) communication supported by the ultra-dense network (UDN) has received a great deal of attention as a means to satisfy stringent requirements in throughput, latency, and energy consumption in 6G. In the UDN supported by THz beamforming, handover, an action to change the base station (BS) serving the user, occurs frequently due to the small cell coverage and sudden line-of-sight (LoS) link blockage caused by the interruption of obstacles. To ensure the seamless connectivity in the THz UDN, mobility management, the process to identify the link deterioration and perform the handover, should be performed quickly and accurately. An aim of this paper is to propose a novel computer vision (CV)-aided framework to proactively control the mobility management in the THz communication regime. In our framework referred to as proactive computer vision-aided mobility management (P-CVMM), the position of the user (i.e., distance and angles) is extracted from the images via deep learning (DL)-based object detector and then exploited in predicting the optimal serving BS (S-BS). Since the sparse geometric channel parameters are immediately obtained without the complicated feedback process and the beam heading toward the user's future position can be generated without quantization, P-CVMM can predictively avoid radio link failure (RLF). Using the specially designed dataset called vision objects for mobility management (VOMM), we demonstrate that P-CVMM effectively avoids RLF and achieves more than 90% and 70% reduction in the localization error and handover interruption time (HIT) over the conventional schemes.