Cells on Autopilot: Adaptive Cell (Re)Selection via Reinforcement Learning

TL;DR

This paper introduces CellPilot, a reinforcement learning framework that automatically adapts cell (re)selection parameters in 5G/4G networks, significantly improving network performance over traditional manual configurations.

Contribution

The paper presents a novel RL-based approach for dynamic cell (re)selection parameter tuning, outperforming heuristic methods and demonstrating effective generalization across scenarios.

Findings

RL agent outperforms heuristic reconfigurations by up to 167%

The approach generalizes effectively across different network scenarios

Data-driven tuning improves overall network performance

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.