# AI-Native Network Slicing for 6G Networks arXiv: https://arxiv.org/abs/2105.08576 ยท HTML: https://mlsanity.com/paper/2105.08576/ ## Key facts - Cited by 31 papers in the map (data through 14 Oct 2026). - Builds on 7 of its 29 references that are in the map. - First posted on arXiv 18 May 2021. ## Builds on (most cited first, 7 of 7) - [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019) - [Towards 6G Networks: Use Cases and Technologies](https://mlsanity.com/paper/1903.12216/) (2019) - [Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning](https://mlsanity.com/paper/2012.01991/) (2020) - [White Paper on Broadband Connectivity in 6G](https://mlsanity.com/paper/2004.14247/) (2020) - [Software Defined Space-Air-Ground Integrated Vehicular Networks: Challenges and Solutions](https://mlsanity.com/paper/1703.02664/) (2017) - [Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in Space-Air-Ground Integrated Network](https://mlsanity.com/paper/2010.01471/) (2020) - [Slicing-Based AI Service Provisioning on Network Edge](https://mlsanity.com/paper/2105.07052/) (2021) ## Built on by (most cited first, 30 of 31) - [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022) - [Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler](https://mlsanity.com/paper/2311.17451/) (2023) - [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023) - [Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond](https://mlsanity.com/paper/2306.00419/) (2023) - [Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices](https://mlsanity.com/paper/2310.13981/) (2023) - [Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things](https://mlsanity.com/paper/2402.02506/) (2024) - [Enhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration](https://mlsanity.com/paper/2307.09151/) (2023) - [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023) - [Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inference](https://mlsanity.com/paper/2406.14328/) (2024) - [The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks](https://mlsanity.com/paper/2408.00540/) (2024) - [Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications](https://mlsanity.com/paper/2301.00516/) (2023) - [Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach](https://mlsanity.com/paper/2405.05748/) (2024) - [Agoran: An Agentic Open Marketplace for 6G RAN Automation](https://mlsanity.com/paper/2508.09159/) (2025) - [Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity](https://mlsanity.com/paper/2303.01013/) (2023) - [Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing](https://mlsanity.com/paper/2307.09494/) (2023) - [Slicing for AI: An Online Learning Framework for Network Slicing Supporting AI Services](https://mlsanity.com/paper/2411.02412/) (2024) - [An Agentic AI Control Plane for 6G Network Slice Orchestration, Monitoring, and Trading](https://mlsanity.com/paper/2602.13227/) (2026) - [On Identifying Adversarial Intent Injection in AI-Native 6G Networks](https://mlsanity.com/paper/2609.12144/) (2026) - [VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT](https://mlsanity.com/paper/2210.08132/) (2022) - [Deep Edge Intelligence: Architecture, Key Features, Enabling Technologies and Challenges](https://mlsanity.com/paper/2210.12944/) (2022) - [Reinforcement Learning Agent Design and Optimization with Bandwidth Allocation Model](https://mlsanity.com/paper/2211.12987/) (2022) - [Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks](https://mlsanity.com/paper/2301.03358/) (2022) - [Variational Source-Channel Coding for Semantic Communication](https://mlsanity.com/paper/2410.08222/) (2024) - [Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Network](https://mlsanity.com/paper/2410.10451/) (2024) - [A Representation Learning Approach to Feature Drift Detection in Wireless Networks](https://mlsanity.com/paper/2505.10325/) (2025) - [Constellation as a Service: Tailored Connectivity Management in Direct-Satellite-to-Device Networks](https://mlsanity.com/paper/2507.00902/) (2025) - [AI-driven Orchestration at Scale: Estimating Service Metrics on National-Wide Testbeds](https://mlsanity.com/paper/2507.16077/) (2025) - [Interpretable Attention-Based Multi-Agent PPO for Latency Spike Resolution in 6G RAN Slicing](https://mlsanity.com/paper/2602.11076/) (2026) - [Adaptive RAN Slicing Control via Reward-Free Self-Finetuning Agents](https://mlsanity.com/paper/2603.10564/) (2026) - [Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions](https://mlsanity.com/paper/2604.25018/) (2026) ## Abstract (arXiv) With the global roll-out of the fifth generation (5G) networks, it is necessary to look beyond 5G and envision the 6G networks. The 6G networks are expected to have space-air-ground integrated networks, advanced network virtualization, and ubiquitous intelligence. This article presents an artificial intelligence (AI)-native network slicing architecture for 6G networks to enable the synergy of AI and network slicing, thereby facilitating intelligent network management and supporting emerging AI services. AI-based solutions are first discussed across network slicing lifecycle to intelligently manage network slices, i.e., AI for slicing. Then, network slicing solutions are studied to support emerging AI services by constructing AI instances and performing efficient resource management, i.e., slicing for AI. Finally, a case study is presented, followed by a discussion of open research issues that are essential for AI-native network slicing in 6G networks.