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

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.