Using Machine Learning to Improve the Performance of Flying Networks

Abstract

Nowadays, the strong use of mobile devices, the demands of a good Quality of Service (QoS) given to the users and the increase in the number of Temporary Crowded Events (TCEs) are leading to the use of flying networks to serve these needs and new trends. A Traffic-Aware Multi-Tier Flying Network (TMFN) is a two-level architecture, where Flying Mesh Access Points (FMAPs) are used as mobile access points for adapting the service to the users' disposal in the field of these events. The Network Planning algorithm is applied on top of this architecture and presupposes a reconfiguration of the Unmanned Aerial Vehicle (UAV) topology that guarantees a good QoS to the users. This dissertation seeks to define and build an ML model that, applied to this network architecture, allows better results than those obtained by NetPlan, when searching for optimal solutions for all types of scenarios, including dynamic scenarios. To do so, a Deep Reinforcement Learning (DRL) approach will be used. This approach passes through the intersection of an Reinforcement Learning (RL) algorithm, which, through the definition of an objective function that is rewarded or penalised according to the result of certain actions in the environment, will iteratively learn the best way to adapt itself to different scenarios, with Deep Learning (DL) techniques, that extracts inherent characteristics of the network / environment, in order to improve the development of the objective reward function.