# Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach

arXiv: https://arxiv.org/abs/2608.14599 · HTML: https://mlsanity.com/paper/2608.14599/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 48 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Builds on (most cited first, 20 of 20)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model](https://mlsanity.com/paper/2304.11582/) (2023)
- [Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing](https://mlsanity.com/paper/2206.00422/) (2022)
- [Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler](https://mlsanity.com/paper/2311.17451/) (2023)
- [A Scalable and Generalizable Pathloss Map Prediction](https://mlsanity.com/paper/2312.03950/) (2023)
- [PMNet: Robust Pathloss Map Prediction via Supervised Learning](https://mlsanity.com/paper/2211.10527/) (2022)
- [Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks](https://mlsanity.com/paper/2006.09066/) (2020)
- [Distributed Submodular Maximization with Limited Information](https://mlsanity.com/paper/1706.04082/) (2017)
- [Radio Map Prediction from Aerial Images and Application to Coverage Optimization](https://mlsanity.com/paper/2410.17264/) (2024)
- [Geo2SigMap: High-Fidelity RF Signal Mapping Using Geographic Databases](https://mlsanity.com/paper/2312.14303/) (2023)
- [RMTransformer: Accurate Radio Map Construction and Coverage Prediction](https://mlsanity.com/paper/2501.05190/) (2025)
- [Large Vision Model-Enhanced Digital Twin with Deep Reinforcement Learning for User Association and Load Balancing in Dynamic Wireless Networks](https://mlsanity.com/paper/2410.07611/) (2024)
- [ISAC Imaging by Channel State Information using Ray Tracing for Next Generation 6G](https://mlsanity.com/paper/2509.06672/) (2025)
- [Automatic Network Planning with Digital Radio Twin](https://mlsanity.com/paper/2509.12441/) (2025)
- [Spatial Deep Learning for Site-Specific Movement Optimization of Aerial Base Stations](https://mlsanity.com/paper/2312.10490/) (2023)

## Built on by (most cited first, 1 of 1)

- [Should I Use This Synthetic Dataset for Training? How to Test with Minimal Real Data](https://mlsanity.com/paper/2608.27996/) (2026)

## Abstract (arXiv)

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%.