Enabling Effective Multi-Link Data Distribution in NTN-based 6G Networks

Conference: WSA & SCC 2023 - 26th International ITG Workshop on Smart Antennas and 13th Conference on Systems, Communications, and Coding
02/27/2023 at Braunschweig, Germany

Proceedings: ITG-Fb. 308: WSA & SCC 2023

Pages: 5Language: englishTyp: PDF

Authors:
de Cola, Tomaso (Institute of Communications and Navigation, German Aerospace Center (DLR), Oberpfaffenhofen, Germany)

Abstract:
The extreme service requirements posed by 6G are particularly challenging in environments where terrestrial infrastructure is not continuously available, whereby connectivity complements offered by non-terrestrial networks are an appealing option. The 5G standardisation process currently carried out within 3GPP has instead proven the need for non-terrestrial networks and their seamless integration into terrestrial networks, so as to enable several use-cases in the context of un(der) served scenarios and eventually achieve the largely advertised concept of “connecting the unconnected”. In such a context, the data connectivity model is not exclusively based on pushing video contents to demanding users like in typical satellite business, but is embracing more attractive use-cases for the entire 6G ecosystem, i.e. ranging from device manufacturers to cloud/service providers through mobile/satellite operators. In particular, effective data distribution over multiple links, AI-driven network management, routing and resource allocation, as well as cloud-continuum in heterogeneous networks are key objectives for paving the way towards NTN-inclusive 6G systems. Behind the many potentials that such an ambitious network concept offers, important research challenges emerge, hence motivating a multi-folded analysis, including network architecture, protocol, and algorithm optimisations. Some of the most interesting research directions are addressed in this paper, in order to shed some lights into the main elements characterising the considered technology problems and delve into the corresponding solution space. Additionally, an initial evaluation of AI-driven network management and routing approaches is provided.