# Artificial Intelligence-Enabled Intelligent 6G Networks

arXiv: https://arxiv.org/abs/1912.05744 · HTML: https://mlsanity.com/paper/1912.05744/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2019.

## Built on by (most cited first, 28 of 28)

- [Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2406.07213/) (2024)
- [Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks](https://mlsanity.com/paper/2206.02398/) (2022)
- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories](https://mlsanity.com/paper/2404.14238/) (2024)
- [Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks](https://mlsanity.com/paper/2106.09274/) (2021)
- [Optimization Design for Federated Learning in Heterogeneous 6G Networks](https://mlsanity.com/paper/2303.08322/) (2023)
- [Successive Convex Approximation Based Off-Policy Optimization for Constrained Reinforcement Learning](https://mlsanity.com/paper/2105.12545/) (2021)
- [Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges](https://mlsanity.com/paper/2112.04698/) (2021)
- [AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networks](https://mlsanity.com/paper/2207.00415/) (2022)
- [Towards Carbon-Neutral Edge Computing: Greening Edge AI by Harnessing Spot and Future Carbon Markets](https://mlsanity.com/paper/2304.11374/) (2023)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [SQ-GAN: Semantic Image Communications Using Masked Vector Quantization](https://mlsanity.com/paper/2502.09520/) (2025)
- [The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining](https://mlsanity.com/paper/2202.08185/) (2022)
- [Learning the Optimal Path and DNN Partition for Collaborative Edge Inference](https://mlsanity.com/paper/2410.01857/) (2024)
- [AI Empowered Net-RCA for 6G](https://mlsanity.com/paper/2212.00331/) (2022)
- [Knowledge and Data Dual-Driven Channel Estimation and Feedback for Ultra-Massive MIMO Systems under Hybrid Field Beam Squint Effect](https://mlsanity.com/paper/2403.12813/) (2024)
- [Explainable AI for Enhancing Efficiency of DL-based Channel Estimation](https://mlsanity.com/paper/2407.07009/) (2024)
- [AURA: Adaptive Unified Reasoning and Automation with LLM-Guided MARL for NextG Cellular Networks](https://mlsanity.com/paper/2511.17506/) (2025)
- [Proactive and AoI-aware Failure Recovery for Stateful NFV-enabled Zero-Touch 6G Networks: Model-Free DRL Approach](https://mlsanity.com/paper/2103.03817/) (2021)
- [Ensemble Consensus-based Representation Deep Reinforcement Learning for Hybrid FSO/RF Communication Systems](https://mlsanity.com/paper/2108.02551/) (2021)
- [Distributed Collaborative Inference System in Next-Generation Networks and Communication](https://mlsanity.com/paper/2412.12102/) (2024)
- [Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management](https://mlsanity.com/paper/2203.06465/) (2022)
- [Implicit Channel Learning for Machine Learning Applications in 6G Wireless Networks](https://mlsanity.com/paper/2206.12127/) (2022)
- [A Review of the Convergence of 5G/6G Architecture and Deep Learning](https://mlsanity.com/paper/2208.07643/) (2022)
- [Unified, User and Task (UUT) Centered Artificial Intelligence for Metaverse Edge Computing](https://mlsanity.com/paper/2212.09295/) (2022)
- [Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control](https://mlsanity.com/paper/2212.13141/) (2022)
- [Integration of TinyML and LargeML: A Survey of 6G and Beyond](https://mlsanity.com/paper/2505.15854/) (2025)
- [Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks](https://mlsanity.com/paper/2508.19495/) (2025)

## Abstract (arXiv)

With the rapid development of smart terminals and infrastructures, as well as diversified applications (e.g., virtual and augmented reality, remote surgery and holographic projection) with colorful requirements, current networks (e.g., 4G and upcoming 5G networks) may not be able to completely meet quickly rising traffic demands. Accordingly, efforts from both industry and academia have already been put to the research on 6G networks. Recently, artificial intelligence (AI) has been utilized as a new paradigm for the design and optimization of 6G networks with a high level of intelligence. Therefore, this article proposes an AI-enabled intelligent architecture for 6G networks to realize knowledge discovery, smart resource management, automatic network adjustment and intelligent service provisioning, where the architecture is divided into four layers: intelligent sensing layer, data mining and analytics layer, intelligent control layer and smart application layer. We then review and discuss the applications of AI techniques for 6G networks and elaborate how to employ the AI techniques to efficiently and effectively optimize the network performance, including AI-empowered mobile edge computing, intelligent mobility and handover management, and smart spectrum management. Moreover, we highlight important future research directions and potential solutions for AI-enabled intelligent 6G networks, including computation efficiency, algorithms robustness, hardware development and energy management.