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

TL;DR

AutoBS is a reinforcement learning framework that uses digital network twins to optimize base station deployment in 6G networks, achieving near-optimal capacity with significantly reduced computation time.

Contribution

It introduces AutoBS, combining RL and digital twins for fast, scalable, and near-optimal base station deployment in 6G networks.

Findings

Achieves about 95% of exhaustive search capacity in single BS scenarios.

Reduces inference time from hours to milliseconds.

Suitable for real-time, large-scale network deployment.

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.