# Slicing for AI: An Online Learning Framework for Network Slicing Supporting AI Services

arXiv: https://arxiv.org/abs/2411.02412 · HTML: https://mlsanity.com/paper/2411.02412/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 45 references that are in the map.
- First posted on arXiv 20 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [Energy-Efficient Radio Resource Allocation for Federated Edge Learning](https://mlsanity.com/paper/1907.06040/) (2019)
- [AI-Native Network Slicing for 6G Networks](https://mlsanity.com/paper/2105.08576/) (2021)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning](https://mlsanity.com/paper/2202.06439/) (2022)
- [Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services](https://mlsanity.com/paper/2202.06435/) (2022)
- [Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach](https://mlsanity.com/paper/2003.07651/) (2020)
- [On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration](https://mlsanity.com/paper/2211.03088/) (2022)
- [Optimizing AI Service Placement and Resource Allocation in Mobile Edge Intelligence Systems](https://mlsanity.com/paper/2011.05708/) (2020)
- [RAN Resource Slicing in 5G Using Multi-Agent Correlated Q-Learning](https://mlsanity.com/paper/2107.01018/) (2021)
- [A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches](https://mlsanity.com/paper/2208.09125/) (2022)
- [Customized Slicing for 6G: Enforcing Artificial Intelligence on Resource Management](https://mlsanity.com/paper/2102.10498/) (2021)
- [Towards Energy Efficiency in RAN Network Slicing](https://mlsanity.com/paper/2307.07016/) (2023)
- [Joint Network Slicing, Routing, and In-Network Computing for Energy-Efficient 6G](https://mlsanity.com/paper/2401.06306/) (2024)

## Built on by (most cited first, 1 of 1)

- [Structure-Dependent Regret and Constraint Violation Bounds for Online Convex Optimization with Time-Varying Constraints](https://mlsanity.com/paper/2603.14319/) (2026)

## Abstract (arXiv)

The forthcoming 6G networks will embrace a new realm of AI-driven services that requires innovative network slicing strategies, namely slicing for AI, which involves the creation of customized network slices to meet Quality of service (QoS) requirements of diverse AI services. This poses challenges due to time-varying dynamics of users' behavior and mobile networks. Thus, this paper proposes an online learning framework to optimize the allocation of computational and communication resources to AI services, while considering their unique key performance indicators (KPIs), such as accuracy, latency, and cost. We define a problem of optimizing the total accuracy while balancing conflicting KPIs, prove its NP-hardness, and propose an online learning framework for solving it in dynamic environments. We present a basic online solution and two variations employing a pre-learning elimination method for reducing the decision space to expedite the learning. Furthermore, we propose a biased decision space subset selection by incorporating prior knowledge to enhance the learning speed without compromising performance and present two alternatives of handling the selected subset. Our results depict the efficiency of the proposed solutions in converging to the optimal decisions, while reducing decision space and improving time complexity.