Alexandros antzoulatos6/20/2023 ![]() Finally other specific methods are presented, such as generative adversarial networks and unsupervised learning and clustering. Hybrid solutions are presented such as combined analytical and machine learning modelling as well as expert knowledge aided machine learning. Deep reinforcement learning combines deep neural networks and has the benefit that is can operate on non-structured data. Reinforcement learning is concerned about how intelligent agents must take actions in order to maximize a collective reward, e.g. Feed-forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks belong to this family. They are typically used to model complex relationships between input and output parameters of a system or to find patterns in data. A family of neural networks is presented, which are generally speaking, non-linear statistical data modelling and decision making tools. The white paper introduces the main relevant mechanisms in Artificial Intelligence and Machine Learning currently investigated and exploited for 5G and beyond 5G networks. This white paper on AI/ML as enablers of 5G and B5G networks is based on contributions from 5G PPP projects that research, implement and validate 5G and B5G network systems. ![]() ![]() Kaloxylos, Alexandros Gavras, Anastasius Camps Mur, Daniel Ghoraishi, Mir Hrasnica, Halid Use this identifier to quote or link this document: Title:ĪI and ML – Enablers for Beyond 5G Networks
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