  Beyond 5G networks will operate at high frequencies with wide bandwidths.
This brings both opportunities and challenges. Opportunities include high
throughput connectivity with low latency. However, one of the main challenges
in these networks is due to the high path loss at operating frequencies, which
requires network to be deployed densely to provide coverage. Since these cells
have small inter-site-distance (ISD), the dwell-time of the UEs in these cells
are small, thus supporting mobility in these types of dense networks is a
challenge and require frequent beam or cell reassignments. A pro-active
mobility management scheme which exploits the trajectory can provide better
prediction of cells and beams as UEs move in the coverage area. We propose an
AI based method using sequence-to-sequence modeling for the estimation of
handover cells/beams along with dwell-time using the trajectory information of
the UE. Results indicate that for a dense deployment an accuracy of more than
90 percent can be achieved for handover cell estimation with very low mean
absolute error (MAE) for dwell-time.
