标题： 基于贝叶斯优化和强化学习的数据驱动蜂窝移动管理 显示英文标题

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

摘要： 蜂窝网络中的移动性管理由于网络密集化和异构用户移动特性而面临日益增长的复杂性。传统的切换（HO）机制依赖于预定义参数，如A3偏移量和时间触发阈值（TTT），通常无法在不同速度和部署条件下优化移动性能。固定的A3偏移量和TTT配置要么延迟切换，增加无线链路失败（RLF），要么加速它们，导致过多的乒乓效应。为了解决这些挑战，我们提出了两种基于数据驱动的移动性管理方法，利用高维贝叶斯优化（HD-BO）和深度强化学习（DRL）。HD-BO优化HO参数，如A3偏移量和TTT，实现乒乓效应与RLF之间的理想平衡。DRL提供了一种非参数化的方法，允许代理根据实时网络条件选择服务小区。我们使用真实世界的蜂窝部署场景验证了我们的方法，并采用Sionna射线追踪进行特定站点的信道传播建模。结果显示，HD-BO和DRL均优于3GPP设定1（TTT为480毫秒，A3偏移量为3dB）和设定5（TTT为40毫秒，A3偏移量为-1dB）基准。我们将HD-BO与迁移学习相结合，使其能够概括各种用户速度。将相同的迁移学习策略应用于DRL方法可将其训练时间减少2.5倍，同时保持最优的HO性能，表明它能有效适应包括无人机在内的空中用户的移动性。模拟进一步显示，HD-BO比DRL更具有样本效率，使其更适合于训练数据有限的场景。 显示英文摘要

摘要： 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.