# OpenRAN Gym: AI/ML Development, Data Collection, and Testing for O-RAN on PAWR Platforms

arXiv: https://arxiv.org/abs/2207.12362 · HTML: https://mlsanity.com/paper/2207.12362/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [srsLTE: An Open-Source Platform for LTE Evolution and Experimentation](https://mlsanity.com/paper/1602.04629/) (2016)
- [dApps: Distributed Applications for Real-time Inference and Control in O-RAN](https://mlsanity.com/paper/2203.02370/) (2022)
- [Open, Programmable, and Virtualized 5G Networks: State-of-the-Art and the Road Ahead](https://mlsanity.com/paper/2005.10027/) (2020)
- [Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation](https://mlsanity.com/paper/2110.10617/) (2021)
- [OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN](https://mlsanity.com/paper/2201.05632/) (2022)
- [OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation with AI in O-RAN](https://mlsanity.com/paper/2202.10318/) (2022)

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

- [Network-Optimised Spiking Neural Network (NOS) Scheduling for 6G O-RAN: Spectral Margin and Delay-Tail Control](https://mlsanity.com/paper/2510.11291/) (2025)
- [A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications](https://mlsanity.com/paper/2410.21521/) (2024)
- [SEAL: An Open, Auditable, and Fair Data Generation Framework for AI-Native 6G Networks](https://mlsanity.com/paper/2604.02128/) (2026)
- [Adversarial Water-Filling: Theory, Algorithms, and a Domain-Specific Wireless Foundation Model](https://mlsanity.com/paper/2605.26163/) (2026)
- [Evolving Inspectable O-RAN Slicing xApps with LLMs](https://mlsanity.com/paper/2609.27337/) (2026)

## Abstract (arXiv)

Open Radio Access Network (RAN) architectures will enable interoperability, openness and programmable data-driven control in next generation cellular networks. However, developing and testing efficient solutions that generalize across heterogeneous cellular deployments and scales, and that optimize network performance in such diverse environments is a complex task that is still largely unexplored. In this paper we present OpenRAN Gym, a unified, open, and O-RAN-compliant experimental toolbox for data collection, design, prototyping and testing of end-to-end data-driven control solutions for next generation Open RAN systems. OpenRAN Gym extends and combines into a unique solution several software frameworks for data collection of RAN statistics and RAN control, and a lightweight O-RAN near-real-time RAN Intelligent Controller (RIC) tailored to run on experimental wireless platforms. We first provide an overview of the various architectural components of OpenRAN Gym and describe how it is used to collect data and design, train and test artificial intelligence and machine learning O-RAN-compliant applications (xApps) at scale. We then describe in detail how to test the developed xApps on softwarized RANs and provide an example of two xApps developed with OpenRAN Gym that are used to control a network with 7 base stations and 42 users deployed on the Colosseum testbed. Finally, we show how solutions developed with OpenRAN Gym on Colosseum can be exported to real-world, heterogeneous wireless platforms, such as the Arena testbed and the POWDER and COSMOS platforms of the PAWR program. OpenRAN Gym and its software components are open-source and publicly-available to the research community. By guiding the readers through running experiments with OpenRAN Gym, we aim at providing a key reference for researchers and practitioners working on experimental Open RAN systems.