6G BRAINS | Bring Reinforcement-learning Into Radio Light Network for Massive Connections

Original description

Ubiquitous smart wireless connectivity is critical for future large-scale industrial tasks, services, assets and devices. Very significantly improved connectivity needs to be unlocked through novel spectrum combinations and the fully autonomous management of the underlying network resources by applying online AI at multiple decision layers. 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. We propose 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 proposed solution will be validated by proof-of-concept trials. The primary and secondary applications of THz and OWC technologies for a very broad spectrum of scenarios will be validated at BOSCH’s self-contained smart factory. The developed technologies will be widely applicable to various vertical sectors such as Industry 4.0, intelligent transportation, eHealth, etc. In particular, new business opportunities emerging in 6G BRAINS will be identified for follow-up exploitation activities. The results of 6G BRAINS are expected to create a solid basis for future projects and global standardisation for B5G and 6G technologies in areas relevant to industrial environments.

Status

SIGNED

Url

https://cordis.europa.eu/project/id/101017226

Call topic

ICT-52-2020

Update Date

27-10-2022