A Deep Learning Approach to User Allocation in a 5th Generation Network
Ioannis Konstantoulas, Iliana Loi, Kyriakos Sgarbas, Απόστολος Γκάμας, Christos Bouras · 2024
In the current 5G cellular telecommunication networks, resource allocation is a main optimization target to meet the demands of the network and its users.In this work, we focus on optimizing resource allocation in 5G Multiple Input Multiple Output (MIMO) networks with the assistance of Deep Learning Artificial Neural Networks.To meet the complex and dynamic demands of the network and users the models are trained in a realistic scenario using a large variety of user circumstances.A large contingent of Machine Learning model architectures is tried and cataloged comparing their performance and accuracy.