# Inter-Cell Slicing Resource Partitioning via Coordinated Multi-Agent Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2202.12833 · HTML: https://mlsanity.com/paper/2202.12833/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2022.

## Built on by (most cited first, 5 of 5)

- [Advancing RAN Slicing with Offline Reinforcement Learning](https://mlsanity.com/paper/2312.10547/) (2023)
- [Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods](https://mlsanity.com/paper/2401.11731/) (2024)
- [Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning](https://mlsanity.com/paper/2301.03262/) (2023)
- [Inter-Cell Network Slicing With Transfer Learning Empowered Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2306.11552/) (2023)
- [Compositional Learning for Modular Multi-Agent Self-Organizing Networks](https://mlsanity.com/paper/2506.02616/) (2025)

## Abstract (arXiv)

Network slicing enables the operator to configure virtual network instances for diverse services with specific requirements. To achieve the slice-aware radio resource scheduling, dynamic slicing resource partitioning is needed to orchestrate multi-cell slice resources and mitigate inter-cell interference. It is, however, challenging to derive the analytical solutions due to the complex inter-cell interdependencies, interslice resource constraints, and service-specific requirements. In this paper, we propose a multi-agent deep reinforcement learning (DRL) approach that improves the max-min slice performance while maintaining the constraints of resource capacity. We design two coordination schemes to allow distributed agents to coordinate and mitigate inter-cell interference. The proposed approach is extensively evaluated in a system-level simulator. The numerical results show that the proposed approach with inter-agent coordination outperforms the centralized approach in terms of delay and convergence. The proposed approach improves more than two-fold increase in resource efficiency as compared to the baseline approach.