Knowledge Graph-Based approach for Sustainable 6G End-to-End System Design

2025-07-11

AI Summary

Existing research lacks a systematic methodology linking 6G technological enablers to the Sustainable Development Goals (SDGs), particularly their social dimensions. Method: This paper proposes a knowledge-graph-based, end-to-end co-design paradigm for 6G, establishing—for the first time—a formal mapping between technical enablers and Key Values (KVs) along with their quantifiable Key Value Indicators (KVIs). The framework unifies modeling of use-case KPIs, multidimensional sustainability objectives, enabler capabilities/dependencies/maturity levels, and Hexa-X-II design principles. Contribution/Results: Applied to a collaborative mobile robotics use case, the method efficiently identifies 82 critical enablers and validates efficacy via proof-of-concept experiments. Results demonstrate significantly improved alignment between technical solutions and sustainability goals—including subjective social metrics—thereby providing a scalable, methodology-driven foundation for designing green, inclusive, and trustworthy 6G systems.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Mechanism Design
• Machine Learning: Graph-based Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Previous generations of cellular communication, such as 5G, have been designed with the objective of improving key performance indicators (KPIs) such as throughput, latency, etc. However, to meet the evolving KPI demands as well as the ambitious sustainability targets for the ICT industry, 6G will need to be designed differently. Concretely, 6G will need to consider both the performance and sustainability targets for the various use cases it will serve. Moreover, like previous generations, 6G will have various candidate technological enablers, making the design space of the system even more complex. Furthermore, given the subjective nature of the sustainability indicators, in particular social sustainability, there is a significant gap in literature on how technical enablers and 6G System design can be linked to them. Hence, in this article a novel method for 6G end-to-end (E2E) system design based on Knowledge graphs (KG) has been introduced. It considers as its input: the use case KPIs, use case sustainability requirements expressed as Key Values (KV) and KV Indicators (KVIs), the ability of the technological enablers to satisfy these KPIs and KVIs, the 6G system design principles defined in Hexa-X-II project, the maturity of a technological enabler and the dependencies between the various enablers. As part of the KG method, a novel approach for determining the key values a technological enabler addresses, has also been introduced. The effectiveness of the KG method was demonstrated by its application in designing the 6G E2E system for the cooperating mobile robot use case defined in the Hexa-X-II project, where 82 enablers were selected. Lastly, results from proof-of-concept demonstrations for a subset of the selected enablers have also been provided, which reinforce the efficacy of the KG method for designing a sustainable 6G system.

Problem

• Designing 6G systems balancing performance and sustainability targets
• Linking technical enablers to subjective sustainability indicators
• Managing complex 6G design space with multiple technological enablers

Innovation

• Knowledge Graph-based 6G system design
• Integrates KPIs and sustainability Key Values
• Links technical enablers to sustainability indicators