AI for 5G - How ML techniques improve the 5G network

5G is a technology that is designed to connect virtually everyone and everything with each other, including machines, objects, and devices. In this talk, you will explore how ML techniques can improve the 5G network.

Jan 26, 2023, 5:30 – 6:30 PM

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Key Themes

Machine Learning

About this event

NOTICE: This event is a VIRTUAL event. The link to the event is provided upon registration - you will see a Join virtual event here! button on this page.


5G is a technology that is designed to connect virtually everyone and everything with each other, including machines, objects, and devices. In this talk, we will explore how we are using ML techniques to improve the 5G network, and will dive into deep learning and deep reinforcement learning algorithms. We will finalise with an outlook of our current work on Trustworthy AI for “6G for Connected Sky”.


Time:

18:30 – 18:40: Intro by Sören Gunia (Co-Founder,CHRO)

18:40 – 19:10: „A.I. for 5G – how we use ML techniques to improve the 5G network”  Speakers: Dr.-Ing Alberto Viseras (Solutions Architect and a Senior Project Owner), Daniel Wessel (AI Cluster Lead) & Florian Geiser (Tech Specialist Embedded & AI)

19:10 – 19:30: Q&A


RSVP now!

Speaker

  • Dr.-Ing. Alberto Viseras

    Motius

    Solutions Architect and a Senior Project Owner

Facilitators

  • Daniel Bilic

    GDSC Munich Advisor

  • Hamudi Naanaa

    TUM

    M.Sc. Computer Science

Organizers

  • Philip Rehker

    GDSC Lead

  • Tan-Colin Wei

    GDSC Lead

  • Layla Zadina

    Core Team Member

  • Florian Hoppe

    Core Team Member

  • Justus Beck

    Core Team Member

  • Mohammad (Hamudi) Naanaa

    Core Team Member

  • Maria Pospelova

    Core Team Member

  • Philipp Zagar

    Core Team Member

  • Simon Huang

    Core Team Member

  • Florian Donhauser

    Core Team Member

  • Muhtasham Oblokulov

    MunichNLP

    Core Team Member

  • Maksym Bondarenko

    TUM

    Core Team Member

  • Alexander Wiener

    Core Team Member

  • Yassine Souissi

    Core Team Member

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