  The upsurge in wireless devices and real-time service demands force the move
to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz)
bands combined with the beamforming technology offer significant performance
enhancements for ultra-dense networks (UDNs). Unfortunately, shrinking cell
coverage and severe penetration loss experienced at higher spectrum render
mobility management a critical issue in UDNs, especially optimizing beam
blockages and frequent handover (HO). Mobility management challenges have
become prevalent in city centres and urban areas. To address this, we propose a
novel mechanism driven by exploiting wireless signals and on-road surveillance
systems to intelligently predict possible blockages in advance and perform
timely HO. This paper employs computer vision (CV) to determine obstacles and
users' location and speed. In addition, this study introduces a new HO event,
called block event {BLK}, defined by the presence of a blocking object and a
user moving towards the blocked area. Moreover, the multivariate regression
technique predicts the remaining time until the user reaches the blocked area,
hence determining best HO decision. Compared to typical wireless networks
without blockage prediction, simulation results show that our BLK detection and
PHO algorithm achieves 40\% improvement in maintaining user connectivity and
the required quality of experience (QoE).
