Skip to Main content Skip to Navigation

Cognitive management of self organized radio networks of fifth generation

Abstract : The pressure on operators to improve the network management efficiency is constantly growing for many reasons: the user traffic that is increasing very fast, higher end users expectations, emerging services with very specific requirements. Self-Organizing Networks (SON) concept was introduced by the 3rd Generation Partnership Project as a promising solution to simplify the operation and management of complex networks. Many SON modules are already being deployed in today’s networks. Such networks are known as SON enabled networks, and they have proved to be useful in reducing the complexity of network management. However, SON enabled networks are still far from realizing a network that is autonomous and self-managed as a whole. In fact, the behavior of the SON functions depends on the parameters of their algorithm, as well as on the network environment where it is deployed. Besides, SON objectives and actions might be conflicting with each other, leading to incompatible parameter tuning in the network. Each SON function hence still needs to be itself manually configured, depending on the network environment and the objectives of the operator. In this thesis, we propose an approach for an integrated SON management system through a Cognitive Policy Based SON Management (C-PBSM) approach, based on Reinforcement Learning (RL). The C-PBSM translates autonomously high level operator objectives, formulated as target Key Performance Indicators (KPIs), into configurations of the SON functions. Furthermore, through its cognitive capabilities, the C-PBSM is able to build its knowledge by interacting with the real network. It is also capable of adapting with the environment changes. We investigate different RL approaches, we analyze the convergence time and the scalability and propose adapted solutions. We tackle the problem of non-stationarity in the network, notably the traffic variations, as well as the different contexts present in a network. We propose as well an approach for transfer learning and collaborative learning. Practical aspects of deploying RL agents in real networks are also investigated under Software Defined Network (SDN) architecture.
Complete list of metadata

Cited literature [124 references]  Display  Hide  Download
Contributor : ABES STAR :  Contact
Submitted on : Wednesday, January 16, 2019 - 4:23:12 PM
Last modification on : Saturday, June 25, 2022 - 9:12:19 PM


Version validated by the jury (STAR)


  • HAL Id : tel-01983683, version 1



Tony Daher. Cognitive management of self organized radio networks of fifth generation. Networking and Internet Architecture [cs.NI]. Université Paris-Saclay, 2018. English. ⟨NNT : 2018SACLT023⟩. ⟨tel-01983683⟩



Record views


Files downloads