ABSTRACT
The Open Radio Access Network (O-RAN) Alliance is an industry-led standardization effort, with the main objective of evolving the Radio Access Network (RAN) to be open, intelligent, interoperable, and autonomous to support the ever growing need of improved performance and flexibility in mobile networks. This paper introduces an extension to Network Simulator 3 (ns-3) which mimics the behavior and components of the O-RAN Alliance’s O-RAN architecture. In this paper, we will describe the O-RAN architecture, our model in ns-3, and a Long Term Evolution (LTE) case study that utilizes Machine Learning (ML) and its integration with ns-3. At the end of this paper, the reader will have a general understanding of O-RAN and the capabilities of our fully simulated contribution so it can be leveraged to design and evaluate O-RAN-based solutions.
- [n. d.]. Open Neural Network Exchange Intermediate Representation (ONNX IR) Specification. https://github.com/onnx/onnx/blob/main/docs/IR.mdGoogle Scholar
- 2021. MobiSys ’21: Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services (Virtual Event, Wisconsin). Association for Computing Machinery, New York, NY, USA.Google Scholar
- Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/ Software available from tensorflow.org.Google Scholar
- Luca Baldesi, Francesco Restuccia, and Tommaso Melodia. 2022. ChARM: NextG Spectrum Sharing Through Data-Driven Real-Time O-RAN Dynamic Control. In IEEE INFOCOM 2022 - IEEE Conference on Computer Communications. 240–249. https://doi.org/10.1109/INFOCOM48880.2022.9796985Google ScholarDigital Library
- Leonardo Bonati, Michele Polese, Salvatore D’Oro, Stefano Basagni, and Tommaso Melodia. 2022. OpenRAN Gym: An Open Toolbox for Data Collection and Experimentation with AI in O-RAN. In 2022 IEEE Wireless Communications and Networking Conference (WCNC 2022). 518–523. https://doi.org/10.1109/WCNC51071.2022.9771908Google ScholarDigital Library
- Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems.CoRR abs/1512.01274 (2015). http://dblp.uni-trier.de/db/journals/corr/corr1512.html#ChenLLLWWXXZZ15Google Scholar
- Estefanía Coronado, Shuaib Siddiqui, and Roberto Riggio. 2022. Roadrunner: O-RAN-based Cell Selection in Beyond 5G Networks. In NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium. 1–7. https://doi.org/10.1109/NOMS54207.2022.9789832Google ScholarDigital Library
- Konstantinos Dimou, Min Wang, Yu Yang, Muhammmad Kazmi, Anna Larmo, Jonas Pettersson, Walter Muller, and Ylva Timner. 2009. Handover within 3GPP LTE: Design Principles and Performance. In 2009 IEEE 70th Vehicular Technology Conference Fall. 1–5. https://doi.org/10.1109/VETECF.2009.5378909Google ScholarCross Ref
- Piotr Gawłowicz and Anatolij Zubow. 2019. ns-3 Meets OpenAI Gym: The Playground for Machine Learning in Networking Research. In Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (Miami Beach, FL, USA) (MSWIM ’19). Association for Computing Machinery, New York, NY, USA, 113–120. https://doi.org/10.1145/3345768.3355908Google ScholarDigital Library
- Richard D Hipp. [n. d.]. SQLite. https://www.sqlite.org/index.htmlGoogle Scholar
- Andrea Lacava, Michele Polese, Rajarajan Sivaraj, Rahul Soundrarajan, Bhawani Shanker Bhati, Tarunjeet Singh, Tommaso Zugno, Francesca Cuomo, and Tommaso Melodia. 2022. Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures. arXiv preprint arXiv:2209.14171 (2022).Google Scholar
- MATLAB. 2010. version 7.10.0 (R2010a). The MathWorks Inc., Natick, Massachusetts.Google Scholar
- Microsoft. [n. d.]. ONNX Runtime. https://onnxruntime.ai/.Google Scholar
- Subhadeep Mukhopadhyay. 2022. InfoGram and Admissible Machine Learning. Machine Learning 111, 1 (01 Jan 2022), 205–242. https://doi.org/10.1007/s10994-021-06121-4Google ScholarDigital Library
- O-RAN Alliance. [n. d.]. O-RAN Alliance. https://www.o-ran.orgGoogle Scholar
- O-RAN Alliance. 2018. O-RAN: Towards an Open and Smart RAN. White Paper. Open RAN (O-RAN) Alliance. https://assets-global.website-files.com/60b4ffd4ca081979751b5ed2/60e5afb502810a0947b3b9d0_O-RAN%2BWP%2BFInal%2B181017.pdfGoogle Scholar
- O-RAN Alliance. 2020. O-RAN Use Cases and Deployment Scenarios. White Paper. Open RAN (O-RAN) Alliance. https://assets-global.website-files.com/60b4ffd4ca081979751b5ed2/60e5aff9fc5c8d496515d7fe_O-RAN%2BUse%2BCases%2Band%2BDeployment%2BScenarios%2BWhitepaper%2BFebruary%2B2020.pdfGoogle Scholar
- O-RAN Alliance. 2021. O-RAN Minimum Viable Plan and Acceleration towards Commercialization. White Paper. Open RAN (O-RAN) Alliance. https://assets-global.website-files.com/60b4ffd4ca081979751b5ed2/61199f8adc85474118cf6969_O-RAN%20Minimum%20Viable%20Plan%20and%20Acceleration%20towards%20Commercialization%20White%20Paper%2029%20June%202021.pdfGoogle Scholar
- O-RAN Working Group 1. 2021. O-RAN Information Model and Data Models 1.0. Technical Specification. Open RAN (O-RAN) Alliance.Google Scholar
- O-RAN Working Group 1. 2021. O-RAN Operations and Maintenance Architecture. Technical Specification. Open RAN (O-RAN) Alliance.Google Scholar
- O-RAN Working Group 1. 2021. O-RAN Operations and Maintenance Interface. Technical Specification. Open RAN (O-RAN) Alliance.Google Scholar
- O-RAN Working Group 1. 2022. O-RAN Architecture Description. Technical Specification. Open RAN (O-RAN) Alliance. Version 7.0.Google Scholar
- O-RAN Working Group 1. 2022. Use Cases Detailed Specification. Technical Specification. Open RAN (O-RAN) Alliance. Version 9.0.Google Scholar
- O-RAN Working Group 2. 2022. O-RAN A1 Interface: Application Protocol. Technical Specification. Open RAN (O-RAN) Alliance.Google Scholar
- O-RAN Working Group 2. 2022. O-RAN Non-RT RIC Architecture. Technical Specification. Open RAN (O-RAN) Alliance.Google Scholar
- O-RAN Working Group 3. 2022. Near-Real-time RAN Intelligent Controller Architecture & E2 General Aspects and Principles. Technical Specification. Open RAN (O-RAN) Alliance.Google Scholar
- O-RAN Working Group 3. 2022. O-RAN E2 Application Protocol (E2AP). Technical Specification. Open RAN (O-RAN) Alliance.Google Scholar
- O-RAN Working Group 3. 2022. O-RAN E2 Service Model (E2SM) KPM. Technical Specification. Open RAN (O-RAN) Alliance.Google Scholar
- O-RAN Working Group 3. 2022. O-RAN Near-RT RIC Architecture. Technical Specification. Open RAN (O-RAN) Alliance.Google Scholar
- Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32. Curran Associates, Inc., 8024–8035. http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdfGoogle ScholarDigital Library
- Michele Polese, Leonardo Bonati, Salvatore DOro, Stefano Basagni, and Tommaso Melodia. 2022. ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms. IEEE Transactions on Mobile Computing (2022), 1–14. https://doi.org/10.1109/TMC.2022.3188013Google ScholarDigital Library
- Michele Polese, Leonardo Bonati, Salvatore D’oro, Stefano Basagni, and Tommaso Melodia. 2022. Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges. ArXiv abs/2202.01032 (2022).Google Scholar
- Iago Rego, Lucas Medeiros, Pedro Alves, Mateus Goldbarg, Vitor Lopes, Daniel Flor, Wysterlanya Barros, Vinícius Filho, Vicente Sousa, Eduardo Aranha, Allan Martins, Marcelo Fernandes, Ramon Fontes, and Augusto Neto. 2022. Prototyping Near-Real Time RIC O-RAN xApps for Flexible ML-based Spectrum Sensing. In 2022 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). 137–142. https://doi.org/10.1109/NFV-SDN56302.2022.9974940Google ScholarCross Ref
- George F. Riley and Thomas R. Henderson. 2010. The ns-3 Network Simulator. Springer Berlin Heidelberg, Berlin, Heidelberg, 15–34. https://doi.org/10.1007/978-3-642-12331-3_2Google ScholarCross Ref
- Hao Yin, Pengyu Liu, Keshu Liu, Liu Cao, Lytianyang Zhang, Yayu Gao, and Xiaojun Hei. 2020. ns3-ai: Fostering Artificial Intelligence Algorithms for Networking Research. In Proceedings of the 2020 Workshop on ns-3 (Gaithersburg, MD, USA) (WNS3 2020). Association for Computing Machinery, New York, NY, USA, 57–64. https://doi.org/10.1145/3389400.3389404Google ScholarDigital Library
Index Terms
- O-RAN with Machine Learning in ns-3
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