.png){kind=icon}
.png){kind=icon}
Return
AI-Driven Mobility Management in 5G and 6G Wireless Networks: A Survey
.png){kind=icon}
OA
AI
H
A
N
M
DOI：10.3390/fi18080425.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Next-generation wireless systems are becoming increasingly complex, and there is a growing need for intelligent mobility management mechanisms that can ensure service continuity while making efficient use of network resources. In 5G and future 6G networks, dense small-cell deployments, heterogeneous architectures, and highly mobile users mean that frequent handovers (HOs), uneven traffic distribution, and variable network conditions often lead to degraded user experience, higher signalling overhead, and inefficient use of resources. Because user movement continuously redistributes traffic across cells, effective mobility management is inseparable from load balancing, and the HO process serves as the primary mechanism through which the network manages both. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), offer an opportunity to transform mobility management from reactive to predictive, since data-driven solutions can forecast user movement, fine-tune HO execution, and dynamically allocate radio resources. This paper presents a comprehensive survey of AI-enabled mobility management strategies for 5G, Beyond 5G, and upcoming 6G networks, with particular attention to HO optimization and load balancing. The surveyed literature is organized around the complete lifecycle of AI-enabled mobility management, from mobility prediction and HO decision-making through parameter optimization and execution to KPI monitoring and model updating. This structure is used to classify existing frameworks according to their architectures, learning approaches, and optimization goals. The survey then examines how intelligent HO schemes address critical issues such as load balancing, interference mitigation, connection reliability, and quality-of-service maintenance, and compares conventional and AI-based methods against standardized key performance indicators for mobility robustness, resource efficiency, and service continuity. Finally, the paper discusses unresolved problems and emerging trends, including federated learning, multi-connectivity, and non-terrestrial integration, that will shape the evolution of autonomous mobility management solutions for future wireless networks.
Keywords:
handover
load balancing
heterogeneous networks
5G networks
mobility management
artificial intelligence
deep learning
machine learning
intelligent wireless networks
Journal
IF:
3.6
Papers:
1.1K
Citations:
6.5K
Organization
Cited Papers