# On the Implementation of a Reinforcement Learning-based Capacity Sharing Algorithm in O-RAN

arXiv: https://arxiv.org/abs/2207.10390 · HTML: https://mlsanity.com/paper/2207.10390/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 21 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [Deep Reinforcement Learning for Resource Management in Network Slicing](https://mlsanity.com/paper/1805.06591/) (2018)

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

- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)

## Abstract (arXiv)

The capacity sharing problem in Radio Access Network (RAN) slicing deals with the distribution of the capacity available in each RAN node among various RAN slices to satisfy their traffic demands and efficiently use the radio resources. While several capacity sharing algorithmic solutions have been proposed in the literature, their practical implementation still remains as a gap. In this paper, the implementation of a Reinforcement Learning-based capacity sharing algorithm over the O-RAN architecture is discussed, providing insights into the operation of the involved interfaces and the containerization of the solution. Moreover, the description of the testbed implemented to validate the solution is included and some performance and validation results are presented.