Publication Date

Spring 2023

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Navrati Saxena

Second Advisor

Abhishek Roy

Third Advisor

Genya Ishigaki


5G V2X, quality of service, handover, recurrent neural networks, traffic load patterns


5G V2X networks transmit large amounts of data with low latency, allowing for real-time communication between vehicles and other infrastructure. In 5G V2X networks, handover is a process that allows a connected vehicle to transfer its con- nection from one base station to another as it moves through the network coverage area. Handover is critical to maintaining the quality of service (QoS) and ensuring uninterrupted communication. The base station load is a critical factor in ensuring reliable and efficient 5G V2X connectivity. Prediction of traffic load on base stations ensure resource optimization and smooth connectivity during handovers. This research predicts the load of a base station using recurrent neural networks on a dataset of traffic loads of base stations spanning over a week. Recurrent neural networks can be used for time series data as they can capture complex patterns in the data, including seasonality, trends, and cyclical patterns. The predicted dynamic load value is then used in a handover algorithm and its effect on the handover performance is measured.