Aug 2026· Future Internet· 0 citations· 97 references
TL;DR
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 is presented.
Abstract
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.
Recently, the development and deployment of intelligent controllers for radio access networks (RAN) has attracted significant attention from network operators and international telecommunications organizations, driven by rapid advances in artificial intelligence. Mobility management plays a fundamental role in ensuring seamless connectivity and service quality in 5G RAN. In fact, optimal control in 5G RAN is highly challenging due to its complex, dynamic, and distributed environment. Many approaches have been proposed to address this problem, particularly those based on deep reinforcement learning (DRL). However, contrary to the dense reward assumption in many DRL-based studies, mobility feedback in practical RAN environments is characteristically sparse and delayed. In this paper, we propose WHO (World Model for Handover Optimization), a novel method designed to bridge the gap between sparse feedback and efficient learning in 5G networks. WHO utilizes a world model to convert event-driven rewards into dense predictive signals, facilitating robust multi-agent optimization. Field experiments involving 13 base stations and 39 cells show that the proposed method significantly improves handover performance and network stability compared to conventional DRL approaches, achieving 19–40% higher prediction precision and up to 32% improvement in key performance indicators (KPIs).
Uyen Thi Thu Truong, Doan Van Nguyen, Do Ngoc Tuan et al.· International Conference on...· 0 citations
Simulation results position D3QN-PER as a strong candidate for deployment as a near-RT RIC xApp within the O-RAN architecture, advancing the vision of AI-native mobility management for 6G.
Kalpesh Popat, Divyakant T. Meva· Telecommunications Systems· 0 citations
A proactive mitigation framework that applies the unified Autoregressive Recurrent Neural Network (AR-RNN) that significantly improves network reliability, reducing the network outage probability by up to 50% compared to standard reactive handover procedures.
Khoa Nguyen Dang Dinh, P. Fazio, Miroslav Voznák· PLoS ONE· 0 citations
The 5G wireless networks are fast with low latency and support a very large number of connected devices, making it possible to support advanced applications and services. Such environment demands efficient management of network resources that can be achieved by evaluation of traffic load, distribution of devices, and state of signals to ensure quality-of-service (QoS) and network performance. Conventional resource allocation and mobility management techniques tend to have challenges of flexibility, computational effectiveness, as well as optimal choice of devices in disparate network conditions. In order to address these issues, this paper introduces a multi-stage resource management system that is adaptive in 5G networks to address QoS and mobility efficiency issues. The framework combines the set of network parameters with Adaptive Multivariate Kernel Resource Estimation (AMKRE) in case of assessing and normalizing available resources. Resource-Aware Device Selection (ORADS) is used to select the appropriate devices and then it is followed by Robust Filter-Based Rank Evaluation (RFBRE) where devices are ranked on the basis of reliability and link quality. The algorithm that optimizes the resources distribution taking into consideration the space constraints is known as DistanceAware Adaptive Particle Swarm Optimization (DAAPSO), and the algorithm that provides the continuity of mobility is known as Hybrid Predictive Soft Handover Control (HPSHC). The multi-dimensional paradigm improves the resource utilization, quality-of-service (QoS) provisioning, and mobility management in 5G networks.
G. Ramasamy, C. Chandrasekar· International journal of com...· 0 citations
The rapid deployment of fifth-generation (5G) mobile communication systems has significantly transformed wireless connectivity by supporting ultra-high data rates, ultra-low latency, massive machine-type communications, and heterogeneous Internet of Things (IoT) applications. However, the unprecedented growth in mobile traffic, dynamic user mobility, and diversified quality-of-service requirements have introduced substantial challenges in traffic prediction, congestion management, spectrum utilization, and network resource allocation. Conventional optimization techniques often fail to adapt to the highly dynamic and nonlinear characteristics of modern 5G environments. Deep learning has emerged as an effective paradigm for intelligent traffic optimization by learning complex spatial-temporal traffic patterns from large-scale network data and enabling proactive decision-making. Advanced architectures such as Long Short-Term Memory networks, Convolutional Neural Networks, Graph Neural Networks, Autoencoders, and Deep Reinforcement Learning provide enhanced capabilities for traffic forecasting, dynamic routing, load balancing, network slicing, edge intelligence, and energy-efficient resource management. This paper investigates the integration of deep learning algorithms into 5G traffic optimization frameworks, presents a comprehensive review of recent developments, proposes an intelligent AI-driven optimization architecture, evaluates major performance metrics, and discusses implementation challenges, scalability issues, and future research directions toward autonomous next-generation mobile networks.
Kamal N, Thirupathi Sundararajulu, Ashok Kumar R et al.· International journal of com...· 0 citations
Ultra-dense 5G networks require advanced traffic steering to maintain performance and balance load amid growing user and base station (gNB) densities. Traditional heuristics such as nearest-base-station and Signal-to-Interference-plus-Noise Ratio (SINR)-based selection provide simple solutions but struggle to adapt to dynamic user mobility, diverse traffic, and fluctuating radio conditions at the mobility-control level, often leading to inefficient handovers and degraded network quality. We propose a deep reinforcement learning (DRL) framework to dynamically tune a global handover hysteresis margin that governs handover triggering decisions, optimizing handover success, reducing failures, and enhancing throughput and fairness. Implemented in Python using Stable Baselines3 and NumPy, our custom simulation environment models key mobility-related 5G dynamics at a high level, including user mobility, pathloss-based signal degradation, and interference. We evaluate DRL agents-Deep Q-Network (DQN) and Proximal Policy Optimization (PPO)-against heuristic and hysteresis-based baselines. Results show that DRL-based hysteresis optimization provides strong and robust performance under the considered ultra-dense mobility conditions in handover success rate, average SINR, throughput, and fairness, with PPO demonstrating the most consistent behavior across configurations. This work offers a reproducible simulation framework for further research into adaptive mobility management.
Damianos Diasakos, V. Kokkinos, C. Bouras et al.· International Conference on...· 0 citations
