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Lifecycle Management of Trustworthy AI Models in 6G Networks: The REASON Approach

Juan Parra-Ullauri, Xueqing Zhou, Shadi Moazzeni, Rasheed Hussain, Xenofon Vasilakos, Yulei Wu, Renjith Baby, M M Hassan Mahmud, Gabriele Incorvaia, Darryl Hond, Hamid Asgari, Andrea Tassi, Daniel Warren, Dimitra Simeonidou

TL;DR

The paper tackles the challenge of integrating AI into 6G networks in a trustworthy, lifecycle-managed way. It introduces the REASON architecture, comprising AI Orchestration (AIO), Cognition (COG), and AI Monitoring (AIM), all underpinned by Digital Twins to enable real-time validation, testing, and feedback. Key contributions include a formal lifecycle management framework, privacy-preserving and explainable AI components, formal verification and testing approaches, and a DT-enabled xAPP instantiation demonstrating feasibility. The work advances practical, governance-focused AI deployment in 6G, addressing reliability, transparency, privacy, and regulatory compliance in dynamic network environments.

Abstract

Artificial Intelligence (AI) is expected to play a key role in 6G networks including optimising system management, operation, and evolution. This requires systematic lifecycle management of AI models, ensuring their impact on services and stakeholders is continuously monitored. While current 6G initiatives introduce AI, they often fall short in addressing end-to-end intelligence and crucial aspects like trust, transparency, privacy, and verifiability. Trustworthy AI is vital, especially for critical infrastructures like 6G. This paper introduces the REASON approach for holistically addressing AI's native integration and trustworthiness in future 6G networks. The approach comprises AI Orchestration (AIO) for model lifecycle management, Cognition (COG) for performance evaluation and explanation, and AI Monitoring (AIM) for tracking and feedback. Digital Twin (DT) technology is leveraged to facilitate real-time monitoring and scenario testing, which are essential for AIO, COG, and AIM. We demonstrate this approach through an AI-enabled xAPP use case, leveraging a DT platform to validate, explain, and deploy trustworthy AI models.

Lifecycle Management of Trustworthy AI Models in 6G Networks: The REASON Approach

TL;DR

The paper tackles the challenge of integrating AI into 6G networks in a trustworthy, lifecycle-managed way. It introduces the REASON architecture, comprising AI Orchestration (AIO), Cognition (COG), and AI Monitoring (AIM), all underpinned by Digital Twins to enable real-time validation, testing, and feedback. Key contributions include a formal lifecycle management framework, privacy-preserving and explainable AI components, formal verification and testing approaches, and a DT-enabled xAPP instantiation demonstrating feasibility. The work advances practical, governance-focused AI deployment in 6G, addressing reliability, transparency, privacy, and regulatory compliance in dynamic network environments.

Abstract

Artificial Intelligence (AI) is expected to play a key role in 6G networks including optimising system management, operation, and evolution. This requires systematic lifecycle management of AI models, ensuring their impact on services and stakeholders is continuously monitored. While current 6G initiatives introduce AI, they often fall short in addressing end-to-end intelligence and crucial aspects like trust, transparency, privacy, and verifiability. Trustworthy AI is vital, especially for critical infrastructures like 6G. This paper introduces the REASON approach for holistically addressing AI's native integration and trustworthiness in future 6G networks. The approach comprises AI Orchestration (AIO) for model lifecycle management, Cognition (COG) for performance evaluation and explanation, and AI Monitoring (AIM) for tracking and feedback. Digital Twin (DT) technology is leveraged to facilitate real-time monitoring and scenario testing, which are essential for AIO, COG, and AIM. We demonstrate this approach through an AI-enabled xAPP use case, leveraging a DT platform to validate, explain, and deploy trustworthy AI models.

Paper Structure

This paper contains 34 sections, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. 6G Networks and AI
  4. Trustworthy AI
  5. Related Work
  6. Trustworthy AI for 6G Networks: The REASON Approach
  7. REASON Approach towards AI-Native 6G
  8. Lifecycle Management of Trustworthy AI Models
  9. AIO
  10. AIM
  11. COG
  12. Privacy-preservation
  13. Explainability
  14. Formally Verifiable AI
  15. Verification by Testing
  16. ...and 19 more sections

Figures (9)

  • Figure 1: End-to-end AI Plane: AIO, COG, AIM and DT
  • Figure 1: End-to-end AI Plane: AIO, COG, AIM and DT
  • Figure 2: Example Deployment of an AI Pipeline using AIO, COG, AIM and the mATRIC DT
  • Figure 2: Example Deployment of an AI Pipeline using AIO, COG, AIM and the mATRIC DT
  • Figure 3: Testbed Deployment of an MLOps Pipeline with Trustworthy Evaluation
  • ...and 4 more figures