Machine Learning Based Predictive Handover in Unmanned Aerial Systems Communication

The aerial component of urban mobility, known as Urban Air Mobility (UAM), is being developed as a solution to the increasing transportation challenges faced by urban areas such as traffic congestion, air pollution, and limited mobility options. UAM aims to provide a fast, efficient, secure, and sustainable mode of transportation in cities while reducing the need for ground-based infrastructure. Integrating UAM into the existing civil aviation system is a complex and long-term process that requires overcoming various challenges and considerations. In this paper, we focus on the communication infrastructure requirements, communication standards, and seamless handover methods in communication networks for UAM integration.