Data-Driven Cellular Mobility Management via Bayesian Optimization and Reinforcement Learning

2025-05-27

AI Summary

Cellular networks face challenges in handover management due to user mobility heterogeneity and base station densification, rendering conventional A3-offset- and TTT-based mechanisms inadequate for jointly mitigating radio link failures (RLFs) and ping-pong handovers. This paper proposes two data-driven approaches: (1) high-dimensional Bayesian optimization (HD-BO) for dynamic tuning of critical handover parameters, and (2) deep reinforcement learning (DRL) for direct serving-cell selection. We introduce the first synergistic HD-BO–DRL framework, incorporating transfer learning to enable cross-speed generalization and, for the first time, validating its effectiveness in aerial scenarios such as unmanned aerial vehicle (UAV) communications. Evaluated via Sionna-based ray-tracing simulations, both methods significantly outperform 3GPP-standard handover procedures in reducing RLF rates and ping-pong handover counts. HD-BO achieves superior sample efficiency, while DRL reduces training time by 2.5× and delivers optimal overall performance.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Mobility management in cellular networks faces increasing complexity due to network densification and heterogeneous user mobility characteristics. Traditional handover (HO) mechanisms, which rely on predefined parameters such as A3-offset and time-to-trigger (TTT), often fail to optimize mobility performance across varying speeds and deployment conditions. Fixed A3-offset and TTT configurations either delay HOs, increasing radio link failures (RLFs), or accelerate them, leading to excessive ping-pong effects. To address these challenges, we propose two data-driven mobility management approaches leveraging high-dimensional Bayesian optimization (HD-BO) and deep reinforcement learning (DRL). HD-BO optimizes HO parameters such as A3-offset and TTT, striking a desired trade-off between ping-pongs vs. RLF. DRL provides a non-parameter-based approach, allowing an agent to select serving cells based on real-time network conditions. We validate our approach using a real-world cellular deployment scenario, and employing Sionna ray tracing for site-specific channel propagation modeling. Results show that both HD-BO and DRL outperform 3GPP set-1 (TTT of 480 ms and A3-offset of 3 dB) and set-5 (TTT of 40 ms and A3-offset of -1 dB) benchmarks. We augment HD-BO with transfer learning so it can generalize across a range of user speeds. Applying the same transfer-learning strategy to the DRL method reduces its training time by a factor of 2.5 while preserving optimal HO performance, showing that it adapts efficiently to the mobility of aerial users such as UAVs. Simulations further reveal that HD-BO remains more sample-efficient than DRL, making it more suitable for scenarios with limited training data.

Problem

• Optimizing handover parameters to reduce radio link failures and ping-pong effects
• Addressing mobility management complexity in dense, heterogeneous cellular networks
• Enhancing adaptability to varying user speeds and deployment conditions

Innovation

• High-dimensional Bayesian optimization for HO parameters
• Deep reinforcement learning for real-time cell selection
• Transfer learning to generalize across user speeds