发表机构
Southeast University; Purple Mountain Laboratories（东南大学; 紫金山实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出地理数据驱动的数字孪生结合深度强化学习的智能基站部署框架，仅用公开地理数据即可实现优化，性能接近理想基准且开销大幅降低。
AI 中文摘要
基站（BS）的部署是决定城市无线网络覆盖范围和容量的关键因素。然而，大规模基站部署优化仍面临挑战，因为它依赖于特定站点的无线电传播和用户空间分布，遗憾的是，这两者在部署前难以获取。为克服这一障碍，我们提出一种智能基站部署框架，该框架将地理数据驱动的无线网络数字孪生（DT）与深度强化学习（DRL）相结合，仅利用公开地理数据即可实现无样本的宏基站部署优化，无需现场测量、真实用户轨迹或详尽的射线追踪。所提出的DT包含一个具有混合输入表示的无样本无线电地图预测模型，可在毫秒级实现千米级的信号强度估计，辅以基于扩散的轨迹合成生成模型，共同表征信道和用户分布。我们将DT作为虚拟训练环境，将基站部署建模为多步骤马尔可夫决策过程（MDP），并通过空间结构化DRL算法求解。我们还进一步融入局部搜索过程和基于Wasserstein距离的部署缓冲区，以高效探索庞大的组合解空间。在真实城市场景中的实验结果表明，地理数据驱动的DT达到了与基于100个样本的预测相当的准确性，而智能基站部署框架实现了理想基准性能的98.9%，同时将优化开销降低了99%以上。
英文摘要
The placement of base station (BS) is a fundamental determinant of coverage and capacity of urban wireless networks. Yet large-scale BS deployment optimization remains challenging due to its dependency on site-specific radio propagation and user spatial distributions, both of which are unfortunately difficult to obtain prior to deployment. To overcome this barrier, we propose an intelligent BS deployment framework that integrates a geographic data-informed wireless network digital twin (DT) with deep reinforcement learning (DRL), enabling sample-free macro BS deployment optimization from solely open geographic data, without on-site measurements, real user trajectories, or exhaustive ray tracing. The proposed DT incorporates a sample-free radio map prediction model with hybrid input representation to achieve kilometer-scale signal strength estimation in milliseconds, complemented by a diffusion-based generative model for trajectory synthesis to collectively characterize channel and user distributions. Leveraging the DT as a virtual training environment, we formulate BS deployment as a multi-step Markov decision process (MDP) and solve it via a spatially structured DRL algorithm. A local search process and a Wasserstein distance-based deployment buffer are further incorporated to efficiently explore the large combinatorial solution space. Experimental results in real-world urban scenarios demonstrate that the geographic data-informed DT attains accuracy comparable to 100-sample-based prediction, and the intelligent BS deployment framework achieves up to 98.9% of the idealized benchmark performance while reducing optimization overhead by over 99%.
