AutoBS: Autonomous Base Station Deployment with Reinforcement Learning and Digital Network Twins
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918025003794432 |
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| author | Lee, Ju-Hyung Molisch, Andreas F. |
| author_facet | Lee, Ju-Hyung Molisch, Andreas F. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19647 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoBS: Autonomous Base Station Deployment with Reinforcement Learning and Digital Network Twins Lee, Ju-Hyung Molisch, Andreas F. Information Theory Artificial Intelligence Machine Learning Networking and Internet Architecture 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. |
| title | AutoBS: Autonomous Base Station Deployment with Reinforcement Learning and Digital Network Twins |
| topic | Information Theory Artificial Intelligence Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2502.19647 |