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: AI/ML Development, Data Collection, and Testing for O-RAN on PAWR Platforms,” Computer Networks, vol. 220, pp. 1-11, January 2023. [pdf] [bibtex]
If you use ns-O-RAN, please reference the following paper:
A. Lacava, M. Polese, R. Sivaraj, R. Soundrarajan, B. S. Bhati, T. Singh, T. Zugno, F. Cuomo, T. Melodia, “Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures,” arXiv:2209.14171 [cs.NI], October 2022. [pdf] [bibtex]
Publications
OpenRAN Gym features the following publications:
L. Bonati, M. Polese, S. D’Oro, S. Basagni, and T. Melodia, “NeutRAN: An Open RAN Neutral Host Architecture for Zero-Touch RAN and Spectrum Sharing,” arXiv:2301.07653 [cs.NI], pp. 1-13, January 2023. [pdf] [bibtex]
L. Bonati, M. Polese, S. D’Oro, S. Basagni, and T. Melodia, “OpenRAN Gym: AI/ML Development, Data Collection, and Testing for O-RAN on PAWR Platforms,” Computer Networks, vol. 220, pp. 1-11, January 2023. [pdf] [bibtex]
A. Lacava, M. Polese, R. Sivaraj, R. Soundrarajan, B. S. Bhati, T. Singh, T. Zugno, F. Cuomo, T. Melodia, “Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures,” arXiv:2209.14171 [cs.NI], October 2022. [pdf] [bibtex]
L. Bonati, M. Polese, S. D’Oro, S. Basagni, and T. Melodia, “Intelligent Closed-loop RAN Control with xApps in OpenRAN Gym,” in Proceedings of European Wireless, Dresden, Germany, September 2022. [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,” IEEE Communications Magazine, November 2022. (Preprint available as arXiv:2203.02370 [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,” IEEE Transactions on Mobile Computing, July 2022. [pdf] [bibtex]
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. [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. [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, by the U.S. Office of Naval Research under Grant N00014-20-1-2132, and by OUSD (R&E) through Army Research Laboratory Cooperative Agreement Number W911NF-19-2-0221.
