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

Master thesis (2020)

Authors

A. Arora Electrical Engineering, Mathematics and Computer Science

Contributors

R. Litjens Network Architectures and Services - EEMCS (mentor)
Haibin Zhang TNO (mentor)
J.H. Weber Discrete Mathematics and Optimization - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 Apoorva Arora
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Published Date

29-10-2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

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.

Files

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- Embargo expired in 01-01-2022