Literature Database Entry

roesler2019steuerung

Sascha Rösler, "Steuerung von Software Defined WLAN durch Machine Learning-Agenten am Beispiel der Kanalvergabe," Bachelor Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), November 2019. (Advisor: Anatolij Zubow; Referees: Anatolij Zubow and Thomas Sikora)

Abstract

The number of WiFi devices all over the world increases. Accordingly, the WiFi infrastructure increases, too. More and more networks are offered. In enterprise networks different WiFi Access Point (AP)s are under the same control. The coverage of the various APs can overlap. This thesis offers solutions to find out the best channel allocation for a WiFi enterprise network. Therefore, UniFlex a Software-Defined-Network (SDN) framework for WiFi is extended. Machine learning (ML) agents are connected to UniFlex via OpenAI Gym. For realising this connection, this thesis introduces the framework UniFlex-Gym. This framework is independent from the problem the ML agent has to solve. The conversion of the duty into OpenAI Gym is done by an UniFlex controller. This thesis introduces different ML agents. One agent implements a Thompson sampling algorithm, the other ones use a neuronal network. The target of the agents is detecting the best channel allocation at runtime. Concerning this matter, various experiments are executed. An experiment on a small scale is executed in a real testbed. More extensive ones are simulated. The results show that the agents need a lot of time until they converge to the optimal channel allocation. For example, for a setting of 8 APs the agents take 15 minutes until converging to the best channel allocation. Moving one client in the network can change the best channel allocation. This requires a restart of the searching procedure. To conclude, finding the best channel allocation at runtime by using the introduced agents is not sufficient. As a different approach, the agent based on a neuronal network is extended to train different network configurations at the same time. Each network configuration is a static setting of APs and its allocated clients. In this approach, there is a split of training phase from execution phase. In the training phase, the agent learns from simulation. After that, the agent is able to calculate the best channel allocation for configurations it was not trained for. The agent is capable to estimate the best channel allocation for a given convergence area without any wrong decision. However, the split of training phase and execution phase results in the loss of degrees of freedom in the ML model. The success of the solution depends on the simulation’s model accuracy.

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Sascha Rösler

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@phdthesis{roesler2019steuerung,
    author = {R{\"{o}}sler, Sascha},
    title = {{Steuerung von Software Defined WLAN durch Machine Learning-Agenten am Beispiel der Kanalvergabe}},
    advisor = {Zubow, Anatolij},
    institution = {School of Electrical Engineering and Computer Science (EECS)},
    location = {Berlin, Germany},
    month = {11},
    referee = {Zubow, Anatolij and Sikora, Thomas},
    school = {TU Berlin (TUB)},
    type = {Bachelor Thesis},
    year = {2019},
   }
   
   

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Last modified: 2024-04-19