This paper introduces AutoBS, a reinforcement learning (RL)-based framework for optimal base station (BS) deployment in 6G radio access networks (RAN). AutoBS leverages the Proximal Policy Optimization (PPO) algorithm and fast, site-specific pathloss predictions from PMNet-a generative model for digital network twins (DNT). By efficiently learning deployment strategies that balance coverage and capacity, AutoBS achieves about 95% of the capacity of exhaustive search in single BS scenarios (and in 90% for multiple BSs), while cutting inference time from hours to milliseconds, making it highly suitable for real-time applications (e.g., ad-hoc deployments). AutoBS therefore provides a scalable, automated solution for large-scale 6G networks, meeting the demands of dynamic environments with minimal computational overhead.
@article{arxiv.2502.19647,
title = {AutoBS: Autonomous Base Station Deployment with Reinforcement Learning and Digital Network Twins},
author = {Ju-Hyung Lee and Andreas F. Molisch},
journal= {arXiv preprint arXiv:2502.19647},
year = {2025}
}