用强化学习自动部署基站，速度提升百万倍，接近最优解。
AutoBS: Autonomous Base Station Deployment with Reinforcement Learning and Digital Network Twins
- 结合强化学习与数字孪生模型，智能规划基站位置。
- 单基站时容量达穷举法的95%，多基站达90%。
- 推理时间从小时缩短至毫秒，适合实时部署场景。
本文提出AutoBS，一种基于强化学习的6G无线接入网络基站部署框架。该方法采用近端策略优化（PPO）算法，并利用PMNet——一种用于数字网络孪生（DNT）的生成式模型——实现快速、精准的站点特异性路径损耗预测。通过高效学习覆盖与容量平衡的部署策略，AutoBS在单基站场景下达到穷举搜索约95%的容量，在多基站场景下达到90%，同时将推理时间从数小时压缩至毫秒级，显著提升效率，适用于临时部署等实时应用。该方案为大规模6G网络提供了可扩展、自动化的解决方案，满足动态环境需求且计算开销极低。
原文摘要 · Abstract (English)
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.
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