AutoBS: Autonomous Base Station Deployment with Reinforcement Learning and Digital Network Twins
This provides an automated, scalable solution for large-scale 6G networks, addressing dynamic deployment needs with minimal computational overhead, though it is incremental as it builds on existing RL and digital twin methods.
The paper tackles the problem of optimal base station deployment in 6G radio access networks by introducing AutoBS, a reinforcement learning framework that achieves about 95% of the capacity of exhaustive search in single BS scenarios while reducing inference time from hours to milliseconds.
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.