基于平滑在线学习的平滑切换
Smooth Handovers via Smoothed Online Learning
AI总结：
针对蜂窝网络切换优化难题，本文基于平滑在线学习开展全国性研究，结合设备与小区特征构建动态切换模型，消除对未来信息的强假设，算法契合O-RAN范式且在多场景下性能更优。
摘要：
随着用户对无缝连接的需求提升，切换（HO）已成为蜂窝网络的核心要素。然而，切换优化是一个具有挑战性的问题，移动网络日益增长的复杂性进一步加剧了这一难题。本文通过平滑在线学习（SOL）的视角，开展了首个全国范围的切换优化研究。我们首先分析了来自欧洲一家商业移动网络运营商（MNO）的超4000万用户的大规模数据集，以理解并揭示影响切换的重要特征与性能因素。研究发现表明，切换失败/延迟与无线小区及终端用户设备的特性存在相关性，凸显了当前移动网络中异构性带来的影响。随后，我们将用户设备（UE）-小区关联建模为动态决策，并提出了一个用于实现平滑精准切换的现实系统模型，该模型对现有方法进行了扩展：（i）将设备与小区特征纳入切换优化；（ii）消除了需要未来信号测量值及终端用户移动性先验知识的强假设。我们的算法契合O-RAN范式，即使在复杂环境下也能提供鲁棒的动态悔值保证，并且在真实数据与合成数据的多场景中均表现出更优性能。
英文摘要：
With users demanding seamless connectivity, handovers (HOs) have become a fundamental element of cellular networks. However, optimizing HOs is a challenging problem, further exacerbated by the growing complexity of mobile networks. This paper presents the first countrywide study of HO optimization, through the prism of Smoothed Online Learning (SOL). We first analyze an extensive dataset from a commercial mobile network operator (MNO) in Europe with more than 40M users, to understand and reveal important features and performance impacts on HOs. Our findings highlight a correlation between HO failures/delays, and the characteristics of radio cells and end-user devices, showcasing the impact of heterogeneity in mobile networks nowadays. We subsequently model UE-cell associations as dynamic decisions and propose a realistic system model for smooth and accurate HOs that extends existing approaches by (i) incorporating device and cell features on HO optimization, and (ii) eliminating (prior) strong assumptions about requiring future signal measurements and knowledge of end-user mobility. Our algorithm, aligned with the O-RAN paradigm, provides robust dynamic regret guarantees, even in challenging environments, and shows superior performance in multiple scenarios with real-world and synthetic data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
