Bringing Reinforcement learning Into Radio Light Network for Massive Connections
Published November 13, 2023 | Version 1.0
Project deliverable Open

D3.3 Multiband characterization of propagation in industry scenarios

Creators

  • 1. Fraunhofer
  • 2. ISEP
  • 3. University of Leicester
  • 4. Robert Bosch
  • 5. Eurescom GmbH

Description

The present report introduces the measurement results and parameter estimation for multi-band channel modelling in Industry scenario. The measurements were conducted in a Bosch factory and in a laboratory at Fraunhofer facilities, both in Germany. After the measurements, the data needed to be analysed and prepared for the calibration of the channel modelling task presented in Deliverable D3.4. This deliverable presents validation research to enable new cross-frequency services for the industry environment but in the same way, these results are essential for development of new cross-frequency services in other areas such as V2V or Smart Home. The understanding of the different physical propagation properties of the frequency bands from Sub-6 GHz to THz and OWC enables the creation of a unique framework for further developments in 6G, which is currently unique in the world.

Files

D3.3 Multiband characterization of propagation in industry environments_V1.pdf

Files (4.8 MB)

Additional details

Funding

6G BRAINS – Bring Reinforcement-learning Into Radio Light Network for Massive Connections 101017226
European Commission

Dates

Submitted
2023-11-09