# Holistic Network Virtualization and Pervasive Network Intelligence for 6G

arXiv: https://arxiv.org/abs/2301.00519 · HTML: https://mlsanity.com/paper/2301.00519/

## Key facts

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

## Builds on (most cited first, 30 of 93)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018)
- [Trained Ternary Quantization](https://mlsanity.com/paper/1612.01064/) (2016)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Continuous Deep Q-Learning with Model-based Acceleration](https://mlsanity.com/paper/1603.00748/) (2016)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)

## Built on by (most cited first, 14 of 14)

- [Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges](https://mlsanity.com/paper/2406.07857/) (2024)
- [Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things](https://mlsanity.com/paper/2402.02506/) (2024)
- [User-centric Immersive Communications in 6G: A Data-oriented Framework via Digital Twin](https://mlsanity.com/paper/2410.02688/) (2024)
- [Artificial Intelligence for Web 3.0: A Comprehensive Survey](https://mlsanity.com/paper/2309.09972/) (2023)
- [Adaptive Split Learning over Energy-Constrained Wireless Edge Networks](https://mlsanity.com/paper/2403.05158/) (2024)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformer](https://mlsanity.com/paper/2402.16868/) (2024)
- [Digital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communication](https://mlsanity.com/paper/2311.12223/) (2023)
- [t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Driving](https://mlsanity.com/paper/2410.09747/) (2024)
- [VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Network](https://mlsanity.com/paper/2505.07892/) (2025)
- [Fast AI Model Partition for Split Learning over Edge Networks](https://mlsanity.com/paper/2507.01041/) (2025)
- [User-Centric Communication Service Provision for Edge-Assisted Mobile Augmented Reality](https://mlsanity.com/paper/2509.25905/) (2025)
- [Digital Twin-Based 3D Map Management for Edge-Assisted Mobile Augmented Reality](https://mlsanity.com/paper/2305.16571/) (2023)
- [Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Network](https://mlsanity.com/paper/2410.10451/) (2024)

## 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.