Self-organization for 5G and beyond mobile networks using reinforcement learning

Valente Klaine, Paulo Henrique (2019) Self-organization for 5G and beyond mobile networks using reinforcement learning. PhD thesis, University of Glasgow.

Abstract

The next generations of mobile networks 5G and beyond, must overcome current networks limitations as well as improve network performance.
Some of the requirements envisioned for future mobile networks are: addressing the massive growth required in coverage, capacity and traffic; providing better quality of service and experience to end users; supporting ultra high data rates and reliability; ensuring latency as low as one millisecond, among others.
Thus, in order for future networks to enable all of these stringent requirements, a promising concept has emerged, self organising networks (SONs).
SONs consist of making mobile networks more adaptive and autonomous and are divided in three main branches, depending on their use-cases, namely: self-configuration, self-optimisation, and self-healing.
SON is a very promising and broad concept, and in order to enable it, more intelligence needs to be embedded in the mobile network.
As such, one possible solution is the utilisation of machine learning (ML) algorithms.
ML has many branches, such as supervised, unsupervised and Reinforcement Learning (RL), and all can be used in different SON use-cases.

The objectives of this thesis are to explore different RL techniques in the context of SONs, more specifically in self-optimization use-cases.
First, the use-case of user-cell association in future heterogeneous networks is analysed and optimised.
This scenario considers not only Radio Access Network (RAN) constraints, but also in terms of the backhaul.
Based on this, a distributed solution utilizing RL is proposed and compared with other state-of-the-art methods.
Results show that the proposed RL algorithm outperforms current ones and is able to achieve better user satisfaction, while minimizing the number of users in outage.
Another objective of this thesis is the evaluation of Unmanned Aerial vehicles (UAVs) to optimize cellular networks.
It is envisioned that UAVs can be utilized in different SON use-cases and integrated with RL algorithms to determine their optimal 3D positions in space according to network constraints.
As such, two different mobile network scenarios are analysed, one emergency and a pop-up network.
The emergency scenario considers that a major natural disaster destroyed most of the ground network infrastructure and the goal is to provide coverage to the highest number of users possible using UAVs as access points.
The second scenario simulates an event happening in a city and, because of the ground network congestion, network capacity needs to be enhanced by the deployment of aerial base stations.
For both scenarios different types of RL algorithms are considered and their complexity and convergence are analysed.
In both cases it is shown that UAVs coupled with RL are capable of solving network issues in an efficient and quick manner.
Thus, due to its ability to learn from interaction with an environment and from previous experience, without knowing the dynamics of the environment, or relying on previously collected data, RL is considered as a promising solution to enable SON.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: Mobile networks, wireless networks, mobile communications, self organising networks, self-optimisation, machine learning, reinforcement learning.
• Subjects: T Technology > T Technology (General); T Technology > TK Electrical engineering. Electronics Nuclear engineering
• Colleges/Schools: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Supervisor's Name: Imran, Professor Muhammad
• Date of Award: 2019
• Depositing User: Dr. Paulo Henrique / P H Valente Klaine
• Unique ID: glathesis:2019-74295
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 01 Aug 2019 07:31
• Last Modified: 16 Aug 2022 12:21
• Thesis DOI: 10.5525/gla.thesis.74295
• URI: https://theses.gla.ac.uk/id/eprint/74295

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