Literature Database Entry

raddatz2022evaluation


Niels Raddatz, "Evaluation of a reinforcement learning approach for interference management in WiFi," Master's Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), July 2022. (Advisor: Anatolij Zubow; Referees: Falko Dressler and Thomas Sikora)


Abstract

One of the key problems of modern WiFi networks is their increasing density, which lead to higher interferences by devices in overlapping basic service sets (OBSSs). This demands better methods to mitigate these effects. A new idea to do this was proposed in for the upcoming IEEE 802.11be standard. It is called coordinated beamforming, which extends the concept of beamforming within a basic service set (BSS) to devices in OBSSs. The normal beamforming, that exists today, is used to enable parallel data streams, but it could also be used to suppress the radiation pattern of a multi-antenna system in particular directions. With coordinated beamforming two or more access points (APs) in OBSSs can use this approach to reduce their interference on stations (STAs) in the other BSS. But to use this method a nulling configuration is required. This configuration contains all STAs that should be nulled during the upcoming transmission and has to be renewed depending on the participants in every consecutive transmission. To solve the problem of finding an optimal configuration, I use a reinforcement learning (RL) agent that selects the STAs, which should be nulled within every cooperating OBSSs. I have found that it is possible with this approach to generate solutions for that problem, which lead to higher data rates per AP than the common carrier sense multiple access with collision avoidance (CSMA/CA) approach. But these advantages become thinner as larger the number of cooperating APs gets, because the number of devices that can be nulled is limited by the number of antennas. A conclusion that can be made from that findings, is to use that method only for small scale networks if the APs that are used have only four antennas, which is very common today. On the other hand, if more antennas are available that RL method can also be used for larger network sizes.

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Niels Raddatz

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@phdthesis{raddatz2022evaluation,
    author = {Raddatz, Niels},
    title = {{Evaluation of a reinforcement learning approach for interference management in WiFi}},
    advisor = {Zubow, Anatolij},
    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 = {Master's Thesis},
    year = {2022},
   }
   
   

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Last modified: 2024-06-13