A Hybrid deep double-Q networks (DDQN)–bidirectional long short-term memory (Bi-LSTM) Framework that integrates bi-directional mobility prediction and DRL-based adaptive decision-making is introduced that highlights the effectiveness of integrating predictive intelligence with reinforcement learning for reliable mobility management in 5G-Advanced and emerging 6G networks.
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 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.
H. Asif, Abdulraqeb Alhammadi, N. Tarhuni et al.· Future Internet· 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
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
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
The deployment of Ultra-Dense Networks (UDNs) in 5G systems is to meet the growing demand for high data rates and massive connectivity. However, the dense deployment of small cells increases handover frequency, leading to challenges such as handover failures (HOF), unnecessary handovers, and the ping-pong effect, leading to degradeuser Quality of Service (QoS). This paper proposes a velocity-aware adaptive handover control approach for efficient mobility management in 5G ultra-dense networks. The proposed approach dynamically adjusts Handover Control Parameters (HCPs) called Time-to-Trigger (TTT) and Handover Margin (HOM) on the real-time velocity of User Equipment (UE) and signal conditions. The system is modeled as a two-tier heterogeneous network consisting of a macrocell overlaid with multiple small cells, and performance is evaluated using the Cost 231-Hata propagation model. The findings demonstrate that the proposed algorithm significantly reduces the total number of handovers, mitigates the ping-pong effect, and lowers handover failure rates compared to conventional static schemes. The results confirm that velocity-aware adaptive control enhances network reliability, reduces signaling overhead, and improves overall mobility performance in 5G ultra-dense environments.
Halah Hassen Aldumaini, Hanadi Esmeail Yahya, Oloof Ameen Mohmmed et al.· 2026 6th International Confe...· 0 citations