  Leveraging higher frequencies up to THz band paves the way towards a faster
network in the next generation of wireless communications. However, such
shorter wavelengths are susceptible to higher scattering and path loss forcing
the link to depend predominantly on the line-of-sight (LOS) path. Dynamic
movement of humans has been identified as a major source of blockages to such
LOS links. In this work, we aim to overcome this challenge by predicting human
blockages to the LOS link enabling the transmitter to anticipate the blockage
and act intelligently. We propose an end-to-end system of
infrastructure-mounted LiDAR sensors to capture the dynamics of the
communication environment visually, process the data with deep learning and ray
casting techniques to predict future blockages. Experiments indicate that the
system achieves an accuracy of 87% predicting the upcoming blockages while
maintaining a precision of 78% and a recall of 79% for a window of 300 ms.
