AutoBS: Autonomous Base Station Deployment with Reinforcement Learning and Digital Network Twins

Juhyung Lee, Andreas Molisch

Keywords: 6G, Wireless Networks, Digital Network Twins, Base Station Deployment, Network Optimization, Ray Tracing, Reinforcement Learning, Proximal Policy Optimization

TL;DR: AutoBS is an RL-based framework leveraging PPO and digital twins (PMNet) for rapid, near-optimal 6G base station deployment, reducing deployment time from hours to milliseconds and achieving ~95% efficiency of exhaustive search methods.

Abstract: 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.

Submission Number: 14