Multi-agent reinforcement learning with graph q-networks for antenna tuning

M Bouton, J Jeong, J Outes, A Mendo… - NOMS 2023-2023 …, 2023 - ieeexplore.ieee.org
Future generations of mobile networks are expected to contain more and more antennas
with growing complexity and more parameters. Optimizing these parameters is necessary for …

Multi-agent reinforcement learning with common policy for antenna tilt optimization

A Mendo, J Outes-Carnero, Y Ng-Molina… - arXiv preprint arXiv …, 2023 - arxiv.org
This paper presents a method for optimizing wireless networks by adjusting cell parameters
that affect both the performance of the cell being optimized and the surrounding cells. The …

A graph attention learning approach to antenna tilt optimization

Y Jin, F Vannella, M Bouton, J Jeong… - 2022 1st International …, 2022 - ieeexplore.ieee.org
6G will move mobile networks towards increasing levels of complexity. To deal with this
complexity, optimization of network parameters is key to ensure high performance and timely …

Coordinated reinforcement learning for optimizing mobile networks

M Bouton, H Farooq, J Forgeat, S Bothe… - arXiv preprint arXiv …, 2021 - arxiv.org
Mobile networks are composed of many base stations and for each of them many
parameters must be optimized to provide good services. Automatically and dynamically …

QoE-driven antenna tuning in cellular networks with cooperative multi-agent reinforcement learning

X Liu, G Chuai, X Wang, Z Xu, W Gao… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Antenna tuning plays an essential role in ensuring high quality wireless communications.
Targeting for higher Quality of Service (QoS), many existing network antenna tuning …

Online antenna tuning in heterogeneous cellular networks with deep reinforcement learning

E Balevi, JG Andrews - IEEE Transactions on Cognitive …, 2019 - ieeexplore.ieee.org
We aim to jointly optimize antenna tilt angle, and vertical and horizontal half-power
beamwidths of the macrocells in a heterogeneous cellular network (HetNet). The …

Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs

LM Amorosa, M Skocaj, R Verdone… - arXiv preprint arXiv …, 2023 - arxiv.org
The ever-increasing demand for high-quality and heterogeneous wireless communication
services has driven extensive research on dynamic optimization strategies in wireless …

Low Risk Antenna Configurations for Mobile Communication Systems: A Safe Reinforcement Learning Method

Y Zhang, S Wang - IEEE Wireless Communications Letters, 2024 - ieeexplore.ieee.org
Reinforcement learning offers an effective framework for antenna angle setting since it
enables the autonomous and adaptive tuning of antenna parameters based on continuous …

A novel deep reinforcement learning algorithm for online antenna tuning

E Balevi, JG Andrews - 2019 IEEE Global Communications …, 2019 - ieeexplore.ieee.org
The interactions between the cells, most notably due to their coupled interference and the
large number of users, render the optimization of antenna parameters prohibitively complex …

Multi-antenna tuning simulation platform by deep reinforcement learning

Y Zhao, K Zhang, R Han - 2019 IEEE International Conference …, 2019 - ieeexplore.ieee.org
Recently, communication technology is highly developed. The communication convenience
that people enjoy is relying on a large number of base station antenna devices set up by …