The widespread deployment of 5G networks, together with the coexistence of 4G/LTE networks, provides mobile devices a diverse set of candidate cells to connect to. However, associating mobile devices to cells to maximize overall network performance, a.k.a. cell (re)selection, remains a key challenge for mobile operators. Today, cell (re)selection parameters are typically configured manually based on operator experience and rarely adapted to dynamic network conditions. In this work, we ask: Can an agent automatically learn and adapt cell (re)selection parameters to consistently improve network performance? We present a reinforcement learning (RL)-based framework called CellPilot that adaptively tunes cell (re)selection parameters by learning spatiotemporal patterns of mobile network dynamics. Our study with real-world data demonstrates that even a lightweight RL agent can outperform conventional heuristic reconfigurations by up to 167%, while generalizing effectively across different network scenarios. These results indicate that data-driven approaches can significantly improve cell (re)selection configurations and enhance mobile network performance.

翻译：5G网络的广泛部署，与4G/LTE网络的共存，为移动设备提供了多样化的候选小区连接选择。然而，将移动设备与小区关联以最大化整体网络性能——即小区（重）选择——仍然是移动运营商面临的关键挑战。目前，小区（重）选择参数通常基于运营商经验手动配置，很少根据动态网络条件进行调整。在本工作中，我们提出：智能体能否自动学习并调整小区（重）选择参数，以持续提升网络性能？我们提出了一个基于强化学习（RL）的框架CellPilot，通过学习移动网络动态的时空模式，自适应地调整小区（重）选择参数。我们基于真实数据的研究表明，即使是一个轻量级的RL智能体，其性能也可超越传统启发式重配置方法高达167%，并能有效泛化到不同的网络场景。这些结果表明，数据驱动的方法可以显著改善小区（重）选择配置，并提升移动网络性能。