Machine Learning-Based Strategy for Joint User Association and Resource Allocation in Next-Generation Networks

Matheus Alves; Gustavo Broechl; Luna Loyolla; Warley Junior; Marcela Alves; Hugo Kuribayashi

doi:10.5753/jisa.2025.4894

Authors

Matheus Alves Universidade Federal do Sul e Sudeste do Pará https://orcid.org/0009-0007-1350-0097
Gustavo Broechl Universidade Federal do Sul e Sudeste do Pará https://orcid.org/0009-0009-7996-4083
Luna Loyolla Universidade Federal do Sul e Sudeste do Pará https://orcid.org/0009-0008-9337-324X
Warley Junior Universidade Federal do Sul e Sudeste do Pará https://orcid.org/0000-0001-5490-3705
Marcela Alves Universidade Federal do Sul e Sudeste do Pará https://orcid.org/0000-0003-0241-1076
Hugo Kuribayashi Universidade Federal do Sul e Sudeste do Pará https://orcid.org/0000-0003-0805-206X

DOI:

https://doi.org/10.5753/jisa.2025.4894

Keywords:

User Association, Resource Allocation, Machine Learning, Reinforcement Learning

Abstract

This study presents an approach based on Reinforcement Learning (RL) to optimize the orchestration of User Association and Resource Allocation (UARA) mechanisms in next-generation heterogeneous networks, focusing on maximizing user satisfaction. The proposed strategy aims to improve the efficiency of these networks by overcoming operational challenges through user-centered adaptive algorithms. RL algorithms are utilized to rebalance the network load and optimize the distribution of radio resources among User Equipments (UEs), ultimately leading to improved service conditions. The results suggest that the strategic application of RL algorithms can lead to significant improvements compared to traditional methods, such as Max-SINR and Cell Range Expansion (CRE), reaching over 90% user satisfaction, highlighting the relevance of this research for next-generation networks.

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Machine Learning-Based Strategy for Joint User Association and Resource Allocation in Next-Generation Networks

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