Joint Strategy for User Association and Resource Allocation in Next-Generation Mobile Networks

• Matheus Alves Unifesspa
• Gustavo Broechl Unifesspa
• Luna Loyolla Unifesspa
• Warley Junior Unifesspa
• Marcela Alves Unifesspa / UEPA
• Hugo Kuribayashi Unifesspa

DOI: https://doi.org/10.5753/wperformance.2024.2917

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 enhance the efficiency of these networks by overcoming operational challenges through adaptive algorithms centered on the user. 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 the future communications network context.

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