Cells on Autopilot: Adaptive Cell (Re)Selection via Reinforcement Learning
Abstract
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.
Cite
@article{arxiv.2601.04083,
title = {Cells on Autopilot: Adaptive Cell (Re)Selection via Reinforcement Learning},
author = {Marvin Illian and Ramin Khalili and Antonio A. de A. Rocha and Lin Wang},
journal= {arXiv preprint arXiv:2601.04083},
year = {2026}
}
Comments
11 pages, 13 figures, 3 tables, v3: Added analysis of heuristic tuning trade-offs (Config-A vs Config-B) across scenarios with corresponding reference-value table; corrected performance numbers in the conclusion; no change to methodology