# 6G Network AI Architecture for Everyone-Centric Customized Services

arXiv: https://arxiv.org/abs/2205.09944 · HTML: https://mlsanity.com/paper/2205.09944/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2022.

## Built on by (most cited first, 13 of 13)

- [Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach](https://mlsanity.com/paper/2503.15764/) (2025)
- [RadioDiff-k2: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction](https://mlsanity.com/paper/2504.15623/) (2025)
- [Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning](https://mlsanity.com/paper/2210.16118/) (2022)
- [GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers](https://mlsanity.com/paper/2210.17281/) (2022)
- [Optimization Design for Federated Learning in Heterogeneous 6G Networks](https://mlsanity.com/paper/2303.08322/) (2023)
- [Implementation of Big AI Models for Wireless Networks with Collaborative Edge Computing](https://mlsanity.com/paper/2404.17766/) (2024)
- [SANet: A Semantic-aware Agentic AI Networking Framework for Cross-layer Optimization in 6G](https://mlsanity.com/paper/2512.22579/) (2025)
- [Towards Net-Zero Carbon Emissions in Network AI for 6G and Beyond](https://mlsanity.com/paper/2401.01007/) (2023)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [SANNet: A Semantic-Aware Agentic AI Networking Framework for Multi-Agent Cross-Layer Coordination](https://mlsanity.com/paper/2505.18946/) (2025)
- [RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Construction](https://mlsanity.com/paper/2603.18865/) (2026)
- [Statistical QoS Provision in Business-Centric Networks](https://mlsanity.com/paper/2408.15609/) (2024)
- [Integration of TinyML and LargeML: A Survey of 6G and Beyond](https://mlsanity.com/paper/2505.15854/) (2025)

## Abstract (arXiv)

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions.