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Intelligent Channel Assignment for WI-FI System Based on Reinforcement Learning

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F14%3APU110397" target="_blank" >RIV/00216305:26220/14:PU110397 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Intelligent Channel Assignment for WI-FI System Based on Reinforcement Learning

  • Original language description

    This paper focuses on enhanced channel planning for WI-FI systems. Based on a real measurement of the frequency spectrum background, the quality of each channel has been classified. Machine learning algorithms are used to process these data and control the system. Reinforcement learning (a special example of machine learning) is used due to its complexity as a test-trial agents system. In the proposed system punishment and reward schema are utilized making it possible to change channels during the transmission and use the one with minimal interference. The promising increase of SINR up to 5% for outdoor scenarios and up to 30% for indoor scenarios should be applied to improve modulation schemas and increase data throughput.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20201 - Electrical and electronic engineering

Result continuities

  • Project

    <a href="/en/project/ED2.1.00%2F03.0072" target="_blank" >ED2.1.00/03.0072: Centre of sensor, information and communication systems</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2014

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of PIERS 2014 in Guangzhou

  • ISBN

    978-1-934142-28-8

  • ISSN

    1559-9450

  • e-ISSN

  • Number of pages

    4

  • Pages from-to

    2322-2325

  • Publisher name

    Neuveden

  • Place of publication

    Neuveden

  • Event location

    Guangzhou

  • Event date

    Aug 25, 2014

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000393225900515