Recent Developments in Machine Learning Techniques for Handover Optimization in 5G

The advent of fifth-generation (5G) technology in wireless communication systems introduced a new era of connectivity, marked by exceptional capabilities. However, the complexities introduced by heterogeneous network architectures, dynamic radio conditions, and diverse application requirements pose significant challenges to many traditional mechanisms of wireless networks, among which handover. This paper addresses these challenges by delving into the potential for machine learning techniques to optimize handover in 5G networks. We review the latest state-of-the-art machine learning methodologies, focusing on their application across various stages of the handover process. By exploring the synergy between machine learning and handover optimization, this research provides valuable insights into the novel techniques aimed at ensuring robust connectivity and enhanced quality-of-service metrics in dynamic network environments.