mlsanityData to 7 Oct 2026

Holistic Network Virtualization and Pervasive Network Intelligence for 6G

arXiv 2301.00519 ↗First posted 2 Jan 2023cs.NI cs.AIcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 93 of its 254 references that are in the map. First posted on arXiv 2 Jan 2023.

Builds on

93 of 254 references.

References · 93

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  5. 2015Continuous control with deep reinforcement learningcited by 3,279
  6. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
  7. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  8. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  9. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  10. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  11. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
  12. 2019Towards Federated Learning at Scale: System Designcited by 826
38 more
  1. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
  2. 2017BranchyNet: Fast Inference via Early Exiting from Deep Neural Networkscited by 591
  3. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  4. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  5. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  6. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  7. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 225
  8. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
  9. 2018Federated Learning via Over-the-Air Computationcited by 215
  10. 2016Trained Ternary Quantizationcited by 203
  11. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  12. 2020The Computational Limits of Deep Learningcited by 188
  13. 2019A Survey on Distributed Machine Learningcited by 187
  14. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  15. 2019Federated Learning over Wireless Fading Channelscited by 178
  16. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 157
  17. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 150
  18. 2019Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computingcited by 150
  19. 2016Continuous Deep Q-Learning with Model-based Accelerationcited by 149
  20. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
  21. 2017Distributed Deep Neural Networks over the Cloud, the Edge and End Devicescited by 119
  22. 2018Deep Learning in Mobile and Wireless Networking: A Surveycited by 117
  23. 2017Learning to Optimize: Training Deep Neural Networks for Wireless Resource Managementcited by 96
  24. 2019Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligencecited by 75
  25. 2017Deep Learning-Based Communication Over the Aircited by 72
  26. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  27. 2021The Road Towards 6G: A Comprehensive Surveycited by 40
  28. 2019Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?cited by 38
  29. 2021Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directionscited by 38
  30. 2017Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networkscited by 37
  31. 2020Advancements of federated learning towards privacy preservation: from federated learning to split learningcited by 34
  32. 2019Machine Learning in the Aircited by 32
  33. 2018Deep Learning for Launching and Mitigating Wireless Jamming Attackscited by 31
  34. 2021AI-Native Network Slicing for 6G Networkscited by 31
  35. 2013Scalable Object Detection using Deep Neural Networkscited by 30
  36. 2019Artificial Intelligence-Enabled Intelligent 6G Networkscited by 27
  37. 2018Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learningcited by 26
  38. 2019Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systemscited by 19

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Citing papers per year: 2023: 3; 2024: 8; 2025: 3.

14 papers.

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  1. 2024Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challengescited by 10
  2. 2024Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Thingscited by 6
  3. 2024User-centric Immersive Communications in 6G: A Data-oriented Framework via Digital Twincited by 5
  4. 2023Artificial Intelligence for Web 3.0: A Comprehensive Surveycited by 4
  5. 2024Adaptive Split Learning over Energy-Constrained Wireless Edge Networkscited by 4
  6. 2024Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformercited by 2
  7. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  8. 2023Digital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communicationcited by 1
  9. 2024t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Drivingcited by 1
  10. 2025VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Networkcited by 1
  11. 2025Fast AI Model Partition for Split Learning over Edge Networkscited by 1
  12. 2025User-Centric Communication Service Provision for Edge-Assisted Mobile Augmented Realitycited by 1
2 more
  1. 2023Digital Twin-Based 3D Map Management for Edge-Assisted Mobile Augmented Realitycited by 0
  2. 2024Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Networkcited by 0