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Robert Jaschek, "Using Reinforcement Learning for Management of Fixed Wireless Access Networks," Bachelor Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), July 2022. (Advisor: Sebastian Bräuer; Referees: Falko Dressler and Thomas Sikora)


Access networks in densely populated areas with many users are becoming increas- ingly important due to the 32 % annual increase in Internet traffic. Despite the ever-increasing number of users, Internet providers need to ensure a certain level of quality. In this paper, an alternative to installing optical fiber by using a hybrid 5 GHz/60 GHz network is presented. The sub-6 GHz approach for the last hop raises the problem of ensuring fairness and quality of service of users. To optimize the performance of the dense WiFi networks, a central reinforcement learning agent is analyzed to respond to changing conditions of the dense WiFi access network, such as different numbers of users and distances between access points. The results show that a centralized SDN controller using a reinforcement learning agent did not provide results commensurate with the effort. The designed custodies and opti- mizations can be helpful for further investigations with more complex topologies to investigate whether more complex approaches are necessary.

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Robert Jaschek

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    author = {Jaschek, Robert},
    title = {{Using Reinforcement Learning for Management of Fixed Wireless Access Networks}},
    advisor = {Br{\"{a}}uer, Sebastian},
    institution = {School of Electrical Engineering and Computer Science (EECS)},
    location = {Berlin, Germany},
    month = {7},
    referee = {Dressler, Falko and Sikora, Thomas},
    school = {TU Berlin (TUB)},
    type = {Bachelor Thesis},
    year = {2022},

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