# DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing

arXiv: https://arxiv.org/abs/2008.07614 · HTML: https://mlsanity.com/paper/2008.07614/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2108.04087/) (2021)
- [Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets](https://mlsanity.com/paper/2202.13652/) (2022)
- [Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods](https://mlsanity.com/paper/2401.11731/) (2024)
- [Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things](https://mlsanity.com/paper/2407.10987/) (2024)
- [Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning](https://mlsanity.com/paper/2301.03262/) (2023)
- [Deep Reinforcement Learning Based Resource Allocation for Cloud Native Wireless Network](https://mlsanity.com/paper/2305.06249/) (2023)
- [Inter-Cell Network Slicing With Transfer Learning Empowered Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2306.11552/) (2023)

## Abstract (arXiv)

Network slicing enables multiple virtual networks run on the same physical infrastructure to support various use cases in 5G and beyond. These use cases, however, have very diverse network resource demands, e.g., communication and computation, and various performance metrics such as latency and throughput. To effectively allocate network resources to slices, we propose DeepSlicing that integrates the alternating direction method of multipliers (ADMM) and deep reinforcement learning (DRL). DeepSlicing decomposes the network slicing problem into a master problem and several slave problems. The master problem is solved based on convex optimization and the slave problem is handled by DRL method which learns the optimal resource allocation policy. The performance of the proposed algorithm is validated through network simulations.