VIRTUAL NRW Innovations Beyond 5G – Advancing Wireless Network Research

Running

Wednesday, 30 June: 10:00 - 12:00 CEST

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Session Description

PROGRAMME organised by TU Dortmund University

Join here https://tu-dortmund.zoom.us/j/93004906310?pwd=WDZ4TndKZGVCVzRONzVVdXN5MElGQT09

PROGRAMME organised by TU Dortmund University

10 - 10.10 am - Opening
by Prof. Dr.-Ing Christian Wietfeld (Communication Networks Institute, TU Dortmund University)

10.10 - 10.55 am - Research insights into the benefits of millimeter wave communications for mobile networks of beyond 5G
by Karsten Heimann (Competence Center 5G.NRW, Communication Networks Institute, TU Dortmund University)

At the latest since 5G, the millimeter wave (mmWave) domain has received attention for mobile networks due to the large amount of available radio resources. However, the exploitation of this spectrum is still limited, because radio propagation is significantly different from conventional frequency bands. This presentation provides some research insights into the benefits of mmWave communications for cellular networks, especially relevant for vehicular and mobility-related use cases.

10.55 - 11.15 am - SAMUS: Slice-Aware Machine Learning-based Ultra-Reliable Scheduling
by Caner Bektas (Communication Networks Institute, TU Dortmund University)

Machine Learning is essential to provide end-to-end quality of service guarantees within the scope of 5G Network Slicing, e.g. for vertical industries. In this talk, the realization and evaluation of the data-driven and 5G Network Slicing-capable prototype SAMUS is presented.

11.15 - 12.00 pm - Resource-Efficient Vehicle-to-Cloud Communications Leveraging Machine Learning
by Benjamin Sliwa (Collaborative Research Center SFB 876, Communication Networks Institute, TU Dortmund University)

Vehicular data is anticipated to become the "new oil" of the automotive industry, which fuels the emergence of a multitude of crowdsensing-enabled services. However, the tremendous increase in transmitted data represents a massive challenge for the coexistence of different users and services within resource-constrained cellular networks. In this talk, client-based and machine learning-enabled opportunistic networking is proposed as a novel solution approach for achieving a better efficiency of the existing network resources.

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