Enhancing Reliability in 6G Networks Based on User-Driven AI
Year: 2024
Type: article
Abstract: In the sixth-generation (6G) era of mobile networks, services will be around users, following a user-centric approach. Inspired by the fifth-generation (5G) network paradigms, this redesigned architecture will be driven by the openness of the 6G system and the distributed edge-cloud continuum. Artificial Intelligence and Machine Learning (AI/ML) integration to the network brings the latest emerging advancements in the field. Mission-critical applications, such as remote surgery, metaverse, and autonomous vehicles, expect high Quality of Experience (QoE) which can only be ensured through strong network and service reliability. In this paper, we propose a novel framework for ensuring user-centric reliability on 6G networks, based on AI algorithms, that extends over the whole 6G edge-cloud continuum infrastructure. In particular, we present two profiling methods (service and security) that are able to identify and act proactively against malicious activities and infrastructure-related issues using AI, taking into account the unique user characteristics. We also perform an evaluation analysis aiming to designate the need and the invaluable use of the user-centric characteristics, e.g., user position, speed, etc., when trying to optimize the QoE and ensure reliability.
Authors: Betzelos, Christos, Uzunidis, Dimitris, Leligou, Helen, Karkazis, Panagiotis
Institution: University of West Attica
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949265985
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