ADROIT6G DAI-Driven Open and Programmable Architecture for 6G Networks
Authors:
- Christophoros ChristophorouCCChristophoros Christophorou
- Iacovos IoannouIacovos Ioannou
- Vasos VassiliouVVVasos Vassiliou
- John S VardakasJVJohn S Vardakas
- Carla Fabiana ChiasseriniCCCarla Fabiana Chiasserini
- Marius IordacheMIMarius Iordache
- Chaouki Ben IssaidCIChaouki Ben Issaid
- Giulio FranzeseGFGiulio Franzese
Open Access
- 4 December 2023
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 744-750
Abstract
In the upcoming 6G era, mobile networks must deal with more challenging applications (e.g., holographic telepresence and immersive communication) and meet far more stringent application requirements stemming along the edge-cloud continuum. These new applications will create an elevated level of expectations on performance, reliability, ubiquity, trust-worthiness, security, openness, and sustainability, pushing the boundaries of innovation and driving transformational change across the architecture of future mobile networks. Towards this end, ADROIT6G proposes a set of disruptive innovations with a clear vision on setting a 6G network architecture that can be tailored to the requirements of innovative applications and match the ambitious KPls set for 6G networks. More specifically, the key transformations that ADROIT6G considers essential to 6G network evolution are: i) AIIML-powered optimisations across the network, exploring solutions in the “Distributed Artificial Intelligence (DAI)” domain for high performance and automation; ii) Transforming to fully cloud-native network software, which can be implemented across various edge-cloud platforms, with security built integrally into the network user plan; and iii) Soft-ware driven, zero-touch operations and ultimately automation of every aspect of the network and the services it delivers.Keywords
All Related Versions
Funding Information
- European Union (101095363)
This publication has 14 references indexed in Scilit:
- Vision and research directions of 6G technologies and applicationsJournal of King Saud University Computer and Information Sciences, 2022
- SCHEMA: Service Chain Elastic Management with Distributed Reinforcement Learning2021
- 5G D2D Transmission Mode Selection Performance & Cluster Limits Evaluation of Distributed AI and ML Techniques2021
- Performance Evaluation of Transmission Mode Selection in D2D communication2021
- Using UE-VBS for dynamic virtual small cells deployment and backhauling in 5G Ultra-Dense networksComputer Networks, 2021
- Distributed Artificial Intelligence Solution for D2D Communication in 5G NetworksIEEE Systems Journal, 2020
- Stabilization Approaches for Reinforcement Learning-Based End-to-End Autonomous DrivingIEEE Transactions on Vehicular Technology, 2020
- Dynamic selection of Virtual Small Base Station in 5G Ultra-Dense Network using Initializing Matching Connection Algorithm2019
- Selection of UE-based Virtual Small Cell Base Stations using Affinity Propagation Clustering2018
- CelEc framework for reconfigurable small cells as part of 5G ultra-dense networks2017