Publications

 

OpenRAN Gym is an open-source project fostering collaborative, AI-driven and experimental research in the Open RAN ecosystem. The goal of OpenRAN Gym is to bring together researchers from academia and industry to create a vibrant, dynamic, evolving and cooperative ecosystem advancing research and development of cutting-edge and groundbreaking solutions for the Open RAN. We welcome contributions from the community, please follow this link to contribute to OpenRAN Gym.

If you use the components part of OpenRAN Gym, please reference the following paper and the paper relative to the specific component that you are using.

L. Bonati, M. Polese, S. D’Oro, S. Basagni, and T. Melodia, “OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation with AI in O-RAN,” in Proceedings of IEEE WCNC Workshop on Open RAN Architecture for 5G Evolution and 6G, Austin, TX, USA, April 2022. (Preprint available as arXiv:2202.10318 [cs.NI].) [pdf] [bibtex]

 

Publications

OpenRAN Gym features the following publications:

S. D’Oro, L. Bonati, M. Polese, T. Melodia, “OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN,” in Proceedings of IEEE INFOCOM, May 2022. (Preprint available as arXiv:2201.05632 [cs.NI].) [pdf] [bibtex]

S. D’Oro, M. Polese, L. Bonati, H. Cheng, and T. Melodia, “dApps: Distributed Applications for Real-time Inference and Control in O-RAN,” arXiv preprint arXiv:2203.02370, March 2022. [pdf] [bibtex]

M. Polese, L. Bonati, S. D’Oro, S. Basagni, T. Melodia, “Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges,” arXiv:2202.01032 [cs.NI], February 2022. [pdf] [bibtex]

L. Bonati, M. Polese, S. D’Oro, S. Basagni, and T. Melodia, “OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation with AI in O-RAN,” in Proceedings of IEEE WCNC Workshop on Open RAN Architecture for 5G Evolution and 6G, Austin, TX, USA, April 2022. (Preprint available as arXiv:2202.10318 [cs.NI].) [pdf] [bibtex]

M. Polese, L. Bonati, S. D’Oro, S. Basagni, and T. Melodia, “ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms,” arXiv preprint arXiv:2112.09559, January 2022. [pdf] [bibtex]

L. Bonati, S. D’Oro, M. Polese, S. Basagni, and T. Melodia, “Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks,” IEEE Communications Magazine, vol. 59, no. 10, pp. 21–27, October 2021. [pdf] [bibtex]

M. Polese, F. Restuccia, and T. Melodia, “DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks”, in Proceedings of ACM MobiHoc, July 2021. [pdf] [bibtex]

L. Bonati, S. D’Oro, S. Basagni, and T. Melodia, “SCOPE: An Open and Softwarized Prototyping Platform for NextG Systems,” in Proceedings of ACM MobiSys, June 2021. [pdf] [bibtex]

 

Support

OpenRAN Gym is partially supported by the U.S. National Science Foundation under Grants CNS-1925601, CNS-2120447, and CNS-2112471.