A Comprehensive Survey on Machine Learning Methods for Handover Optimization in 5G Networks

• Published in: Electronics (Switzerland)
• Main Author: Thillaigovindhan S.K.; Roslee M.; Mitani S.M.I.; Osman A.F.; Ali F.Z.
• Format: Review
• Language: English
• Published: Multidisciplinary Digital Publishing Institute (MDPI) 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202653108&doi=10.3390%2felectronics13163223&partnerID=40&md5=fa66d08370c38c82ba4a2bab877e4685

One of the key features of mobile networks in this age of mobile communication is seamless communication. Handover (HO) is a critical component of next-generation (NG) cellular communication networks, which requires careful management since it poses several risks to quality-of-service (QoS), including a decrease in average throughput and service disruptions. Due to the dramatic rise in base stations (BSs) and connections per unit area brought about by new fifth-generation (5G) network enablers, such as Internet of things (IoT), network densification, and mm-wave communications, HO management has become more challenging. The degree of difficulty is increased in light of the strict criteria that were recently published in the specifications of 5G networks. In order to address these issues more successfully and efficiently, this study has explored and examined intelligent HO optimization strategies using machine learning models. Furthermore, the significant goal of this review is to present the state of cellular networks as they are now, as well as to talk about mobility and home office administration in 5G alongside the overall features of 5G networks. This work presents an overview of machine learning methods in handover optimization and of the various data availability for evaluations. In the final section, the challenges and future research directions are also detailed. © 2024 by the authors.