# Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul

arXiv: https://arxiv.org/abs/2211.07466 · HTML: https://mlsanity.com/paper/2211.07466/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View](https://mlsanity.com/paper/1804.05057/) (2018)
- [Open, Programmable, and Virtualized 5G Networks: State-of-the-Art and the Road Ahead](https://mlsanity.com/paper/2005.10027/) (2020)
- [AI-enabled Future Wireless Networks: Challenges, Opportunities and Open Issues](https://mlsanity.com/paper/2103.04536/) (2021)
- [Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)](https://mlsanity.com/paper/2201.07385/) (2022)

## Built on by (most cited first, 4 of 4)

- [Attention-based Open RAN Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2306.09490/) (2023)
- [Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2401.06922/) (2024)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learning](https://mlsanity.com/paper/2511.15002/) (2025)

## Abstract (arXiv)

Network slicing envisions the 5th generation (5G) mobile network resource allocation to be based on different requirements for different services, such as Ultra-Reliable Low Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB). Open Radio Access Network (O-RAN), proposes an open and disaggregated concept of RAN by modulizing the functionalities into independent components. Network slicing for O-RAN can significantly improve performance. Therefore, an advanced resource allocation solution for network slicing in O-RAN is proposed in this study by applying Reinforcement Learning (RL). This research demonstrates an RL compatible simplified edge network simulator with three components, user equipment(UE), Edge O-Cloud, and Regional O-Cloud. This simulator is later used to discover how to improve throughput for targeted network slice(s) by dynamically allocating unused bandwidth from other slices. Increasing the throughput for certain network slicing can also benefit the end users with a higher average data rate, peak rate, or shorter transmission time. The results show that the RL model can provide eMBB traffic with a high peak rate and shorter transmission time for URLLC compared to balanced and eMBB focus baselines.