Master of Science (MS)
Machine Learning, Cell Selection, 5G, Vehicular Networks, Handover
This paper proposes a novel approach to handover optimization in fifth generation vehicular networks. A key principle in designing fifth generation vehicular network technology is continuous connectivity. This makes it important to ensure that there are no gaps in communication for mobile user equipment. Handovers can cause disruption in connectivity as the process involves switching from one base station to another. Issues in the handover process include poor load management for moving traffic resulting in low bandwidth or connectivity gaps, too many hops resulting in multiple unneccessary handovers, short dwell times and ineffective base station selection resulting in delays and other connectivity issues. Here, we propose an efficient handover model using trajectory prediction and optimized target base station identification.
Shyamsundar, Pooja, "A Novel Handover Method Using Destination Prediction in 5G-V2X Networks" (2022). Master's Projects. 1100.