@misc{10481/106424,
year = {2025},
url = {https://hdl.handle.net/10481/106424},
abstract = {5G deployments highlight the need for efficient infrastructure optimization. As networks become more complex, traditional algorithms struggle, requiring AI and ML approaches. This paper presents a deep Q-Network (DQN)-based agent for optimal Physical Resource Blocks (PRBs) allocation, focusing on energy saving while ensuring service requirements through interference mitigation and resource allocation. The agent employs a multi-stage reward algorithm to achieve distinct sub-goals and a training process that enables generalizable optimization across all cells in the scenario. The results demonstrate that this design enables the agent to efficiently optimize multiple cells in a coordinated manner.},
organization = {Spanish Government TSI-063000-2021-28},
organization = {NextGenerationEU},
organization = {MICIU/AEI/10.13039/501100011033 PID2022-137329OB-C43},
organization = {ERDF/EU},
publisher = {Springer},
keywords = {5G},
keywords = {Radio Resource Allocation},
keywords = {Deep Q-Networks},
keywords = {6G},
title = {Energy-Efficient Radio Resource Allocation in 5G Using Deep Q-Networks},
author = {Fernández Martínez, David and Chinchilla Romero, Lorena and Muñoz Luengo, Pablo and Ameigeiras Gutiérrez, Pablo José},
}