Base Station Deployment under EMF constrain by Deep Reinforcement learning

TL;DR

This paper introduces a deep reinforcement learning framework combined with a generative adversarial network to optimize base station deployment for 5G/6G networks, balancing coverage and RF-EMF safety efficiently.

Contribution

It presents a novel GAN-DQN approach that accelerates deployment predictions and enables real-time, adaptive base station placement under safety and coverage constraints.

Findings

cGAN reduces inference time from hours to seconds

GAN-DQN enables sequential decision making for deployment

Framework supports real-time network adaptation

Abstract

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.