mlsanityData to 14 Oct 2026

AI-Native Multi-Access Future Networks -- The REASON Architecture

arXiv 2411.06870 ↗First posted 11 Nov 2024cs.NI cs.AI cs.SYcited by 2

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Abstract (arXiv)

The development of the sixth generation of communication networks (6G) has been gaining momentum over the past years, with a target of being introduced by 2030. Several initiatives worldwide are developing innovative solutions and setting the direction for the key features of these networks. Some common emerging themes are the tight integration of AI, the convergence of multiple access technologies and sustainable operation, aiming to meet stringent performance and societal requirements. To that end, we are introducing REASON - Realising Enabling Architectures and Solutions for Open Networks. The REASON project aims to address technical challenges in future network deployments, such as E2E service orchestration, sustainability, security and trust management, and policy management, utilising AI-native principles, considering multiple access technologies and cloud-native solutions. This paper presents REASON's architecture and the identified requirements for future networks. The architecture is meticulously designed for modularity, interoperability, scalability, simplified troubleshooting, flexibility, and enhanced security, taking into consideration current and future standardisation efforts, and the ease of implementation and training. It is structured into four horizontal layers: Physical Infrastructure, Network Service, Knowledge, and End-User Application, complemented by two vertical layers: Management and Orchestration, and E2E Security. This layered approach ensures a robust, adaptable framework to support the diverse and evolving requirements of 6G networks, fostering innovation and facilitating seamless integration of advanced technologies.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 116 references that are in the map. First posted on arXiv 11 Nov 2024.

Builds on

9 of 116 references.

References · 9

  1. 2019The many Shapley values for model explanationcited by 344
  2. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 120
  3. 2021Semantic Communications: Principles and Challengescited by 91
  4. 2020White Paper on Broadband Connectivity in 6Gcited by 10
  5. 2024Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inferencecited by 4
  6. 2024Service Level Agreements and Security SLA: A Comprehensive Surveycited by 3
  7. 2023Towards Bridging the FL Performance-Explainability Trade-Off: A Trustworthy 6G RAN Slicing Use-Casecited by 2
  8. 2024FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deploymentscited by 2
  9. 2023Energy-Efficient Softwarized Networks: A Surveycited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.