Machine learning for handover decision with mobile edge computing in 6G mobile network: a survey

Abstract

The forthcoming Six-Generation (6G) cellular network promises to provide novel developments in network architecture, offering greater data rates and ultra-low latency, ensuring seamless and reliable connectivity with a high quality of service for a massive number of connected devices. Even though efficient handover decisionmaking is one of the critical challenges in 6G networks, especially with high mobility scenarios and complex characterization of future networks. The case becomes more critical with the implementation of Mobile Edge Computing (MEC), which will lead to making the handover decision process more challenging due to its characterization and high requirements. This paper presents a systematic review of the handover decision (HOD) with MEC in 6G networks, providing a deep understanding of the most standing challenges and solutions addressing mobility management issues in 6G mobile networks. Moreover, machine learning (ML) and deep learning (DL) technologies are the key promising solutions for intelligent HOD-making in 6G networks with MEC. Therefore, this research work also aims to give a main focus on studying and highlighting the advanced ML methods that can be used to enhance HOD-making in 6G cellular networks with MEC. Furthermore, a comprehensive review of HOD-based ML solutions is provided to enhance the Quality of Service (QoS) of user experience in Heterogeneous Networks (HetNet), instilling confidence in the paper's findings. Besides, proposed solutions for HODs using ML models with next-generation network requirements and possible technologies are presented. We also describe research challenges and future directions for achieving this study.