Artificial Intelligence-Based Radio Resource Management in Sliced Radio Access Networks
Apoorva Arora (TU Delft - Electrical Engineering, Mathematics and Computer Science)
R. Litjens – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Haibin Zhang – Mentor (TNO)
J.H. Weber – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
In this thesis, we design and assess a multi-slice resource allocation framework that is based on machine learning techniques (subset of artificial intelligence techniques). The proposed framework employs two machine learning techniques namely, artificial neural networks and reinforcement learning for resource management in sliced RAN. Alternative multi-slice resource allocation methods that involve only artificial neural networks but not reinforcement learning are also defined.