6G BRAINS

Bringing Reinforcement learning Into Radio Light Network for Massive Connections

6G BRAINS Bringing Reinforcement learning Into Radio Light Network for Massive Connections

6G BRAINS aims to bring AI-driven multi-agent Deep Reinforcement learning (DRL) to perform resource allocation over and beyond massive machine-type communications with new spectrum links including THz and optical wireless communications (OWC) to enhance the performance with regard to capacity, reliability and latency for future industrial networks.
The project proposes a novel comprehensive cross-layer DRL driven resource allocation solution to support the massive connections over device-to-device (D2D) assisted highly dynamic cell-free network enabled by Sub-6 GHz/mmWave/THz/OWC and high resolution 3D Simultaneous Localization and Mapping (SLAM) of up to 1 mm accuracy.
The enabling technologies in 6G BRAINS focus on four major aspects including disruptive new spectral links, highly dynamic D2D cell-free network modelling, intelligent end-to-end network architecture integrating the multi-agent DRL scheme and AI-enhanced high-resolution 3D SLAM data fusion.
The developed technologies will be widely applicable to various vertical sectors such as Industry 4.0, intelligent transportation, eHealth, etc.

Main Objectives:

  • An AI-driven D2D cell free network architecture for highly dynamic and ultra-dense connectivity
  • AI-based End-to-End (E2E) Directional network slicing with guaranteed QoS over highly dynamic network
  • AI-driven data fusion for the 3D indoor position map through heterogeneous location methods enabling 1cm location position accuracy and 1o orientation accuracy
  • Enhanced new spectrum links: OWC and THzo.

Bringing Reinforcement learning Into Radio Light Network for Massive Connections 6G BRANSEnhanced new spectrum links: OWC and THzo.

Contact at Eurescom: Anastasius Gavras
Project Duration: 01/01/2021  01/03/2023
Coordinator: Anastasius Gavras from Eurescom GmbH 
Budget: 
Number of partners: 
Grant Agreement Number: 101017226