Artificial Intelligence-Based Radio Resource Management in Sliced Radio Access Networks

Artificial Intelligence-Based Radio Resource Management in Sliced Radio Access Networks

Arora, Apoorva (TU Delft Electrical Engineering, Mathematics and Computer Science)

Litjens, R. (mentor)
Zhang, Haibin (mentor)
Weber, J.H. (graduation committee)

Delft University of Technology

Electrical Engineering | Embedded Systems

2020-10-29

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.

Radio access networks
Reinforcement learning
artificial neural networks
Network slicing

http://resolver.tudelft.nl/uuid:2891a444-a714-40c8-8b47-9238bc7d2d77

2022-01-01

Student theses

master thesis

© 2020 Apoorva Arora