Holistic Network Virtualization and Pervasive Network Intelligence for 6G (2301.00519v1)

Abstract: In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive AI. The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G.

• The paper’s main contribution is the proposal of an integrated 6G architecture employing digital twins for flexible and scalable network slicing.
• It demonstrates the dual role of AI by optimizing network management and structuring networks to support AI applications effectively.
• The framework addresses key challenges including resource allocation, security, privacy, and energy efficiency in future 6G environments.

Holistic Network Virtualization and Pervasive Network Intelligence for 6G: A Summary

The paper "Holistic Network Virtualization and Pervasive Network Intelligence for 6G" presents a conceptual framework for 6G networks, focusing on network architecture evolution. It introduces two primary elements: holistic network virtualization and pervasive AI, proposing an innovative integration to address the anticipated demands and challenges of 6G networks.

Key Concepts in the Proposed Architecture

1. Holistic Network Virtualization:
   • Network Slicing and Digital Twin Integration: The architecture extends current network slicing by introducing digital twins, representing both the service provision (network-centric) and user demand (user-centric) aspects. This duality supports nuanced service levels, adaptable to the diverse and dynamic service demands of 6G users.
   • Virtualization for Flexibility and Scalability: With digital twins, the network can emulate various service scenarios, enhancing adaptability to fluctuations in demands and resource availability. This alignment aims to improve resource utilization efficiency and service quality.
2. Pervasive Network Intelligence:
   • AI for Networking and Networking for AI: The paper envisions integrating AI across all network layers, facilitating two-way intelligence. AI for networking involves leveraging AI to optimize network management, including resource allocation and service provisioning. Conversely, networking for AI entails structuring the network to efficiently support AI-based applications and services.
   • Connected AI Models: The document proposes integrated AI modules across the network to enable collaborative and dynamic decision-making processes. This strategy aims to enhance real-time network operations and long-term strategic planning through AI-driven insights.

Implications and Challenges

The proposed architecture is poised to address critical challenges in future networks:

• Service and User Demand Integration: By adopting a holistic virtualization approach, the proposed model can capture and react to both network-side and user-side dynamics, ensuring a balanced and efficient network operation.
• AI Integration Challenges: Deploying pervasive AI across network layers raises issues concerning data acquisition, processing capabilities, and the energy efficiency of AI computations. Moreover, managing AI models' lifecycle within network constraints is complex, necessitating robust AI orchestration frameworks.
• Security and Privacy Concerns: As digital twins entail extensive data collection and representation, ensuring the security and privacy of this data becomes paramount. The architecture must incorporate advanced security architectures to maintain data integrity and confidentiality.

Future Directions

The paper suggests several areas for future research and development:

• Refinement of Virtualization Models: Developing efficient digital twin models that accurately represent user and network dynamics without imposing prohibitive overheads.
• Hybrid Data-Model Driven Approaches: Exploring the integration of classical model-driven methods and emerging data-driven AI approaches to leverage the strengths of both paradigms.
• Flexibility in Resource Provisioning: Investigating adaptive resource management strategies that can dynamically allocate resources based on real-time demands and predicted future needs.

In conclusion, the architecture proposed in this paper offers a structured pathway towards achieving the flexible, scalable, adaptive, and intelligent networks envisioned for 6G. It emphasizes the symbiosis of virtualization and AI, aiming to enable efficient service delivery even in the face of the complex and heterogeneous demands expected in future wireless environments.