# Base Station Deployment under EMF constrain by Deep Reinforcement learning

arXiv: https://arxiv.org/abs/2601.02385 · HTML: https://mlsanity.com/paper/2601.02385/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 54 references that are in the map.
- First posted on arXiv 23 Dec 2025.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [Patch-Based Image Inpainting with Generative Adversarial Networks](https://mlsanity.com/paper/1803.07422/) (2018)
- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [Multi-tier Drone Architecture for 5G/B5G Cellular Networks: Challenges, Trends, and Prospects](https://mlsanity.com/paper/1711.08407/) (2017)
- [Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning](https://mlsanity.com/paper/2202.10968/) (2022)
- [UAV Base Station Location Optimization for Next Generation Wireless Networks: Overview and Future Research Directions](https://mlsanity.com/paper/1812.11826/) (2018)
- [Placement Optimization of Aerial Base Stations with Deep Reinforcement Learning](https://mlsanity.com/paper/1911.08111/) (2019)
- [From Design to Deployment of Zero-touch Deep Reinforcement Learning WLANs](https://mlsanity.com/paper/2207.06172/) (2022)
- [Downlink Training in Cell-Free Massive MIMO: A Blessing in Disguise](https://mlsanity.com/paper/1903.10046/) (2019)
- [Maximizing Mobile Coverage via Optimal Deployment of Base Station and Relays](https://mlsanity.com/paper/1408.6605/) (2014)
- [Machine Learning Based Uplink Transmission Power Prediction for LTE and Upcoming 5G Networks using Passive Downlink Indicators](https://mlsanity.com/paper/1806.06620/) (2018)

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

- [BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding](https://mlsanity.com/paper/2603.18855/) (2026)

## Abstract (arXiv)

As 5G networks rapidly expand and 6G technologies emerge, characterized by dense deployments, millimeter-wave communications, and dynamic beamforming, the need for scalable simulation tools becomes increasingly critical. These tools must support efficient evaluation of key performance metrics such as coverage and radio-frequency electromagnetic field (RF-EMF) exposure, inform network design decisions, and ensure compliance with safety regulations. Moreover, base station (BS) placement is a crucial task in the network design, where satisfying coverage requirements is essential. To address these, based on our previous work, we first propose a conditional generative adversarial network (cGAN) that predicts location specific received signal strength (RSS), and EMF exposure simultaneously from the network topology, as images. As a network designing application, we propose a Deep Q Network (DQN) framework, using the trained cGAN, for optimal base station (BS) deployment in the network. Compared to conventional ray tracing simulations, the proposed cGAN reduces inference and deployment time from several hours to seconds. Unlike a standalone cGAN, which provides static performance maps, the proposed GAN-DQN framework enables sequential decision making under coverage and exposure constraints, learning effective deployment strategies that directly solve the BS placement problem. Thus making it well suited for real time design and adaptation in dynamic scenarios in order to satisfy pre defined network specific heterogeneous performance goals.