Holistic Network Virtualization and Pervasive Network Intelligence for 6G

• URL: http://arxiv.org/abs/2301.00519v1
• Date: Mon, 2 Jan 2023 04:15:33 GMT
• Title: Holistic Network Virtualization and Pervasive Network Intelligence for 6G
• Authors: Xuemin (Sherman) Shen, Jie Gao, Wen Wu, Mushu Li, Conghao Zhou, and Weihua Zhuang
• Abstract summary: 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 artificial intelligence (AI) We aim to inspire further discussions and developments on the potential architecture of 6G.
• Score: 14.35331138476144
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 artificial intelligence (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.

Related papers

• Towards Neural Architecture Search for Transfer Learning in 6G Networks [4.863212763542215]
  We describe and review the state-of-the-art in Neural Architecture Search and Transfer Learning and their applicability in networking. We identify open research challenges and set directions with a specific focus on three main requirements with elements unique to the future network.
  arXiv  Detail & Related papers  (2024-06-04T14:01:03Z)
• Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G [58.440115433585824]
  Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces. While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks. This paper revisits the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems.
  arXiv  Detail & Related papers  (2024-04-29T04:51:05Z)
• Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration [56.330705072736166]
  We propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, and outline a novel cloud-edge-end collaboration paradigm. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system.
  arXiv  Detail & Related papers  (2023-10-26T15:19:40Z)
• Large Language Models Empowered Autonomous Edge AI for Connected Intelligence [51.269276328087855]
  Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence. This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements.
  arXiv  Detail & Related papers  (2023-07-06T05:16:55Z)
• Optimization Design for Federated Learning in Heterogeneous 6G Networks [27.273745760946962]
  Federated learning (FL) is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks. There are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks. In this article, we investigate the optimization approaches that can effectively address the challenges.
  arXiv  Detail & Related papers  (2023-03-15T02:18:21Z)
• Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication [71.21459460829409]
  We introduce an emerging deep learning architecture, known as the transformer, and discuss its potential impact on 6G network design. Specifically, we propose transformer-based solutions for massive multiple-input multiple-output (MIMO) systems and various semantic communication problems in 6G networks.
  arXiv  Detail & Related papers  (2022-05-08T03:22:20Z)
• Introduction to the Artificial Intelligence that can be applied to the Network Automation Journey [68.8204255655161]
  The "Intent-Based Networking - Concepts and Definitions" document describes the different parts of the ecosystem that could be involved in NetDevOps. The recognize, generate intent, translate and refine features need a new way to implement algorithms.
  arXiv  Detail & Related papers  (2022-04-02T08:12:08Z)
• Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications [39.223546118441476]
  6G will revolutionize the evolution of wireless from "connected things" to "connected intelligence" Deep learning and big data analytics based AI systems require tremendous computation and communication resources. edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence.
  arXiv  Detail & Related papers  (2021-11-24T11:47:16Z)
• Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
  Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. In this article, we identify the key requirements and challenges of edge-native AI in 6G.
  arXiv  Detail & Related papers  (2020-10-01T02:16:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.