用强化学习自动优化5G/4G基站连接，提升网络性能。
Cells on Autopilot: Adaptive Cell (Re)Selection via Reinforcement Learning
- 用强化学习分析网络动态，自动调整基站选择参数。
- 实测性能比传统方法最高提升167%。
- 适合需要自适应优化的移动网络运营商。
5G网络广泛部署及与4G/LTE共存，使移动设备面临多样化的候选基站连接选择。然而，如何关联设备与基站以最大化整体网络性能（即基站（重）选择），仍是运营商面临的关键挑战。当前基站（重）选择参数通常基于人工经验配置，很少随动态网络条件调整。本文提出一种基于强化学习的框架CellPilot，通过学习移动网络的时空动态模式，自适应地调节基站选择参数。基于真实世界数据的研究表明，即使轻量级强化学习代理也能在性能上超越传统启发式重配置，最高提升达167%，且在不同网络场景下具有良好的泛化能力。结果表明，数据驱动方法可显著改善基站选择配置，提升移动网络性能。
原文摘要 · Abstract (English)
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.
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